This document summarizes a research article that proposes using a novel clustering-based fuzzy controller for speed control of DC motors. The controller uses a hybrid kernel-based clustering algorithm called KPFCM to identify fuzzy rules and membership functions from motor data. This approach provides an efficient way to model the nonlinear dynamics of DC motors for control purposes. Simulation results show the proposed fuzzy controller achieves better performance than a conventional fuzzy logic controller in terms of rise time, overshoot, settling time, and error metrics. The clustering-based approach reduces computational time compared to traditional fuzzy design methods.
This document discusses using the Levenberg-Marquardt algorithm for forecasting stock exchange share rates on the Karachi Stock Exchange. It provides an overview of artificial neural networks and how they can be used for financial forecasting applications. The Levenberg-Marquardt algorithm is presented as an efficient method for training neural networks to minimize errors through gradient descent. The document applies this method to train a neural network to predict the direction of change in share prices on the Karachi Stock Exchange. The network is trained on historical stock price data and testing shows it can achieve the performance goal of forecasting next day price changes.
Survey on Artificial Neural Network Learning Technique AlgorithmsIRJET Journal
This document discusses different types of learning algorithms used in artificial neural networks. It begins with an introduction to neural networks and their ability to learn from their environment through adjustments to synaptic weights. Four main learning algorithms are then described: error correction learning, which uses algorithms like backpropagation to minimize error; memory based learning, which stores all training examples and analyzes nearby examples to classify new inputs; Hebbian learning, where connection weights are adjusted based on the activity of neurons; and competitive learning, where neurons compete to respond to inputs to become specialized feature detectors through a winner-take-all mechanism. The document provides details on how each type of learning algorithm works.
Comparative study to realize an automatic speaker recognition system IJECEIAES
This document presents a comparative study between an adaptive orthogonal transform method and mel-frequency cepstral coefficients (MFCCs) for automatic speaker recognition. The adaptive orthogonal transform method uses an adaptive operator to extract informative features from input speech signals with minimum dimensions. Experimental results show the adaptive orthogonal transform method achieved 96.8% accuracy using Fourier transform and 98.1% accuracy using correlation, outperforming MFCCs which achieved 49.3% and 53.1% accuracy respectively. The proposed method successfully identified speakers with a recognition rate of 98.1% compared to 53.1% for MFCCs, demonstrating the efficiency of the adaptive orthogonal transform approach.
The document describes a study that uses a hybrid neuro-fuzzy (HNF) approach for automatic generation control (AGC) of a two-area interconnected power system. The HNF controller is designed using an adaptive neuro-fuzzy inference system to control frequency and tie-line power deviations. Simulation results show the HNF controller provides improved dynamic response and faster control compared to a conventional PI controller. The HNF approach can handle non-linearities in power systems while providing faster control than other conventional controllers.
Optimal neural network models for wind speed predictionIAEME Publication
The document presents two neural network models - multilayer perceptron (MLP) and radial basis function (RBF) networks - for wind speed prediction. It evaluates these models on real wind speed data collected over one year from wind farms in Coimbatore, India. The experimental results show that the RBF and MLP models can improve wind speed prediction accuracy compared to other approaches, according to statistical metrics like coefficient of determination, mean absolute error, root mean square error, and mean bias error.
This document summarizes research on neural networks. It discusses the basic structure and components of neural networks, including network topology (feed forward and recurrent), transfer functions, and learning algorithms (supervised, unsupervised, reinforcement). It also overview popular neural network models like multilayer perceptrons, radial basis function networks, Kohonen's self-organizing maps, and Hopfield networks. Finally, it outlines some applications of neural networks such as process control, pattern recognition, and more.
COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN NEURAL NETWORK BASED IDENT...ijcsit
This paper explores the application of artificial neural networks for online identification of a multimachine power system. A recurrent neural network has been proposed as the identifier of the two area, four machine system which is a benchmark system for studying electromechanical oscillations in multimachine power systems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of the paper is on investigating the performance of the variants of the Backpropagation algorithm in training the neural identifier. The paper also compares the performances of the neural identifiers trained using variants of the Backpropagation algorithm over a wide range of operating conditions. The simulation results establish a satisfactory performance of the trained neural identifiers in identification of the test power system.
Efficiency of Neural Networks Study in the Design of TrussesIRJET Journal
The document examines the efficiency of different types of artificial neural networks (ANNs) in the design of trusses. It analyzes generalized regression, radial basis function, and linear layer neural networks using the MATLAB neural network tool. Various truss models are analyzed using the ANNs and STAAD Pro software. The ANNs are trained and tested for interpolation and extrapolation to calculate percentage errors. Parameters like spread constants, number of trainings, number of input/output variables are varied to study their effect on the ANN performance and efficiency. The study aims to determine the most suitable ANN type for truss design based on the percentage error results.
This document discusses using the Levenberg-Marquardt algorithm for forecasting stock exchange share rates on the Karachi Stock Exchange. It provides an overview of artificial neural networks and how they can be used for financial forecasting applications. The Levenberg-Marquardt algorithm is presented as an efficient method for training neural networks to minimize errors through gradient descent. The document applies this method to train a neural network to predict the direction of change in share prices on the Karachi Stock Exchange. The network is trained on historical stock price data and testing shows it can achieve the performance goal of forecasting next day price changes.
Survey on Artificial Neural Network Learning Technique AlgorithmsIRJET Journal
This document discusses different types of learning algorithms used in artificial neural networks. It begins with an introduction to neural networks and their ability to learn from their environment through adjustments to synaptic weights. Four main learning algorithms are then described: error correction learning, which uses algorithms like backpropagation to minimize error; memory based learning, which stores all training examples and analyzes nearby examples to classify new inputs; Hebbian learning, where connection weights are adjusted based on the activity of neurons; and competitive learning, where neurons compete to respond to inputs to become specialized feature detectors through a winner-take-all mechanism. The document provides details on how each type of learning algorithm works.
Comparative study to realize an automatic speaker recognition system IJECEIAES
This document presents a comparative study between an adaptive orthogonal transform method and mel-frequency cepstral coefficients (MFCCs) for automatic speaker recognition. The adaptive orthogonal transform method uses an adaptive operator to extract informative features from input speech signals with minimum dimensions. Experimental results show the adaptive orthogonal transform method achieved 96.8% accuracy using Fourier transform and 98.1% accuracy using correlation, outperforming MFCCs which achieved 49.3% and 53.1% accuracy respectively. The proposed method successfully identified speakers with a recognition rate of 98.1% compared to 53.1% for MFCCs, demonstrating the efficiency of the adaptive orthogonal transform approach.
The document describes a study that uses a hybrid neuro-fuzzy (HNF) approach for automatic generation control (AGC) of a two-area interconnected power system. The HNF controller is designed using an adaptive neuro-fuzzy inference system to control frequency and tie-line power deviations. Simulation results show the HNF controller provides improved dynamic response and faster control compared to a conventional PI controller. The HNF approach can handle non-linearities in power systems while providing faster control than other conventional controllers.
Optimal neural network models for wind speed predictionIAEME Publication
The document presents two neural network models - multilayer perceptron (MLP) and radial basis function (RBF) networks - for wind speed prediction. It evaluates these models on real wind speed data collected over one year from wind farms in Coimbatore, India. The experimental results show that the RBF and MLP models can improve wind speed prediction accuracy compared to other approaches, according to statistical metrics like coefficient of determination, mean absolute error, root mean square error, and mean bias error.
This document summarizes research on neural networks. It discusses the basic structure and components of neural networks, including network topology (feed forward and recurrent), transfer functions, and learning algorithms (supervised, unsupervised, reinforcement). It also overview popular neural network models like multilayer perceptrons, radial basis function networks, Kohonen's self-organizing maps, and Hopfield networks. Finally, it outlines some applications of neural networks such as process control, pattern recognition, and more.
COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN NEURAL NETWORK BASED IDENT...ijcsit
This paper explores the application of artificial neural networks for online identification of a multimachine power system. A recurrent neural network has been proposed as the identifier of the two area, four machine system which is a benchmark system for studying electromechanical oscillations in multimachine power systems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of the paper is on investigating the performance of the variants of the Backpropagation algorithm in training the neural identifier. The paper also compares the performances of the neural identifiers trained using variants of the Backpropagation algorithm over a wide range of operating conditions. The simulation results establish a satisfactory performance of the trained neural identifiers in identification of the test power system.
Efficiency of Neural Networks Study in the Design of TrussesIRJET Journal
The document examines the efficiency of different types of artificial neural networks (ANNs) in the design of trusses. It analyzes generalized regression, radial basis function, and linear layer neural networks using the MATLAB neural network tool. Various truss models are analyzed using the ANNs and STAAD Pro software. The ANNs are trained and tested for interpolation and extrapolation to calculate percentage errors. Parameters like spread constants, number of trainings, number of input/output variables are varied to study their effect on the ANN performance and efficiency. The study aims to determine the most suitable ANN type for truss design based on the percentage error results.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
Performance Analysis of Various Activation Functions in Generalized MLP Archi...Waqas Tariq
This document compares the performance of various activation functions in multilayer perceptron (MLP) neural networks. It analyzes MLP architectures using different activation functions, including bi-polar sigmoid, uni-polar sigmoid, hyperbolic tangent, conic section, and radial basis functions. Based on experiments, hyperbolic tangent performed the best in terms of accuracy, requiring fewer iterations than other functions to solve nonlinear problems. While conic section had the lowest training error, hyperbolic tangent produced the most accurate results during testing. In general, the hyperbolic tangent function achieved high accuracy and is a good choice for most MLP applications.
Expert system design for elastic scattering neutrons optical model using bpnnijcsa
In present paper, a proposed expert system is designed to obtain a trained formulae for the optical model
parameters used in elastic scattering neutrons of light nuclei for (7Li), at energy range between [(1) to
(20)] MeV. A simple algorithm has used to design this expert system, while a multi-layer backwardpropagation
neural network (BPNN) is applied for training and testing the data used in this model. This
group of formulae may get a simple expert system occurring from governing formulae model, and predicts
the critical parameters usually resulted from the complicated computer coding methods. This expert system
may use in nuclear reactions yields in both fission and fusion nature who gives more closely results to the
real model.
Real-time traffic sign detection and recognition using Raspberry Pi IJECEIAES
This document presents a real-time traffic sign detection and recognition system developed using a Raspberry Pi 3 processor. The system uses a Raspberry Pi camera to record real-time video and the TensorFlow machine learning algorithm to detect and identify traffic signs based on a dataset of 500 labeled images across 5 sign classes. The system's accuracy, delay, and reliability were evaluated during testbed implementation considering different environmental and sign conditions. Results showed the system achieved over 90% accuracy on average with a maximum detection delay of 3.44 seconds, demonstrating reliable performance even for broken, faded, or low-light signs. This real-time traffic sign recognition system developed with affordable hardware has potential to increase road safety.
Performance analysis of real-time and general-purpose operating systems for p...IJECEIAES
In general, modern operating systems can be divided into two essential parts, real-time operating systems (RTOS) and general-purpose operating systems (GPOS). The main difference between GPOS and RTOS is the system is time-critical or not. It means that; in GPOS, a high-priority thread cannot preempt a kernel call. But, in RTOS, a low-priority task is preempted by a high-priority task if necessary, even if it’s executing a kernel call. Most Linux distributions can be used as both GPOS and RTOS with kernel modifications. In this study, two Linux distributions, Ubuntu and Pardus, were analyzed and their performances were compared both as GPOS and RTOS for path planning of the multi-robot systems. Robot groups with different numbers of members were used to perform the path tracking tasks using both Ubuntu and Pardus as GPOS and RTOS. In this way, both the performance of two different Linux distributions in robotic applications were observed and compared in two forms, GPOS, and RTOS.
Modeling and simulation of single phase transformer inrush current using neur...Alexander Decker
This document discusses modeling and simulating transformer inrush current using a neural network. It presents equations to calculate inrush current based on transformer parameters. Data on time and flux linkage values during inrush are obtained via simulation in MATLAB and used to train a neural network. The trained network accurately models the inrush current waveform, with an average 1.056% error compared to a semi-analytic solution. Tables and figures show the neural network structure, weights, training error reduction, and ability to model the magnetization curve matching the semi-analytic solution.
Using petri net with inherent fuzzy in the recognition of ecg signalsIAEME Publication
This document presents a method for classifying ECG signals using a fuzzy Petri net with inherent fuzziness. The fuzzy Petri net structure is organized into a neural network called a fuzzy Petri network. Features are extracted from ECG signals using wavelet transforms. A best basis technique is used to select optimal features that reduce the dimensionality of input vectors and complexity. The fuzzy Petri network parameters are learned using backpropagation. The system was tested on 8 classes of ECG beats and achieved accurate classification.
Incorporating Kalman Filter in the Optimization of Quantum Neural Network Par...Waqas Tariq
Kalman filter have been used for the estimation of instantaneous states of linear dynamic systems. It is a good tool for inferring of missing information from noisy measurement. The quantum neural network is another approach to the merging of fuzzy logic with the neural network and that by the investment of quantum mechanics theory in building the structure of neural network. The gradient descent algorithm has been used, widely, in training the neural network, but the problem of local minima is one of the disadvantages of this algorithm. This paper presents an algorithm to train the quantum neural network by using the extended kalman filter.
Nonlinear autoregressive moving average l2 model based adaptive control of no...Mustefa Jibril
This document discusses the use of neural network controllers for nonlinear systems based on nonlinear autoregressive moving average (NARMA) models. It specifically examines using a NARMA-L2 model to design three different neural network controllers for a nerves system based arm position sensor device: 1) a neural network controller with NARMA-L2 system identification, 2) a neural network controller with NARMA-L2 model predictive control, and 3) a neural network controller with NARMA-L2 model reference adaptive control. Simulation results show the neural network controller with NARMA-L2 model reference adaptive control had the best performance for controlling the arm position under different input signals.
High - Performance using Neural Networks in Direct Torque Control for Asynchr...IJECEIAES
This article investigates solution for the biggest problem of the Direct Torque Control on the asynchronous machine to have the high dynamic performance with very simple hysteresis control scheme. The Conventional Direct Torque Control (CDTC) suffers from some drawbacks such as high current, flux and torque ripple, as well as flux control at very low speed. In this paper, we propose an intelligent approach to improve the direct torque control of induction machine which is an artificial neural networks control. The principle, the numerical procedure and the performances of this method are presented. Simulations results show that the proposed ANN-DTC strategy effectively reduces the torque and flux ripples at low switching frequency, compared with Fuzzy Logic DTC and The Conventional DTC.
Black-box modeling of nonlinear system using evolutionary neural NARX modelIJECEIAES
Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system.
This document presents research on an adaptive vague controller based power system stabilizer (AVCPSS). It begins with an abstract that describes using an Adaptive Network Based Vague Inference System (ANVIS) to develop an Adaptive Vague Set Based Controller Power System Stabilizer (AVCPSS) capable of providing stabilization signals over a wide range of operating conditions and disturbances. Section I provides further introduction and background. Section II describes vague set theory and vague controllers, while Section III details the development of a Vague Set Based Controller Power System Stabilizer (VCPSS). Section IV introduces ANVIS for implementing learning and adaptation. Section V discusses the AVCPSS developed using ANVIS. Results in Section VI show the
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Comparison of backstepping, sliding mode and PID regulators for a voltage inv...IJECEIAES
In the present paper, an efficient and performant nonlinear regulator is designed for the control of the pulse width modulation (PWM) voltage inverter that can be used in a standalone photovoltaic microgrid. The main objective of our control is to produce a sinusoidal voltage output signal with amplitude and frequency that are fixed by the reference signal for different loads including linear or nonlinear types. A comparative performance study of controllers based on linear and non-linear techniques such as backstepping, sliding mode, and proportional integral derivative (PID) is developed to ensure the best choice among these three types of controllers. The performance of the system is investigated and compared under various operating conditions by simulations in the MATLAB/Simulink environment to demonstrate the effectiveness of the control methods. Our investigation shows that the backstepping controller can give better performance than the sliding mode and PID controllers. The accuracy and efficiency of the proposed backstepping controller are verified experimentally in terms of tracking objectives.
Comparison of cascade P-PI controller tuning methods for PMDC motor based on ...IJECEIAES
In this paper, there are two contributions: The first contribution is to design a robust cascade P-PI controller to control the speed and position of the permanent magnet DC motor (PMDC). The second contribution is to use three methods to tuning the parameter values for this cascade controller by making a comparison between them to obtain the best results to ensure accurate tracking trajectory on the axis to reach the desired position. These methods are the classical method (CM) and it requires some assumptions, the genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). The simulation results show the system becomes unstable after applying the load when using the classical method because it assumes cancellation of the load effect. Also, an overshoot of about 3.763% is observed, and a deviation from the desired position of about 12.03 degrees is observed when using the GA algorithm, while no deviation or overshoot is observed when using the PSO algorithm. Therefore, the PSO algorithm has superiority as compared to the other two methods in improving the performance of the PMDC motor by extracting the best parameters for the cascade P-PI controller to reach the desired position at a regular speed.
Available transfer capability computations in the indian southern e.h.v power...eSAT Journals
Abstract This paper presents three methods for computing the available transfer capability (ATC). One method is the conventional method known as continuation repeated power flow (CRPF) and other two are the intelligent techniques known as radial basis function neural network (RBFNN), basic adaptive neuro fuzzy inference system (ANFIS). In these two intelligent techniques, the basic ANFIS works with a multiple input single output (MISO) and it is modified as multiple input multiple output (MIMO) ANFIS using the proposed MIMO ANFIS. The main intension of this proposed method is to utilise the significant features of ANFIS with respect to the multiple outputs, as the basic ANFIS has been proved as the best intelligent techniques in the modelling of any application, but it has a disadvantage of single output and this drawback will be overcome using the proposed MIMO ANFIS. In this paper, the latest Indian southern region extra high voltage (SREHV) 72-bus system considered as test system to obtain the ATC computations using three methods. The ATC computations at desired buses are obtained with and without contingencies and compared the conventional ATC computations with the intelligent techniques. The obtained results are scrupulously verified with different test patterns and observed that the accuracy of proposed method is proved as the best as compared to the other methods for computing ATC. In this way, this paper shows a better way to compute ATC for the different open power market system Keywords— Available Transfer Capability, Total Transfer Capability, Open Power Markets, Repeated Power Flow, Radial Basis Function Neural Networks, Adaptive Neuro Fuzzy Inference System etc.
Fuzzy control is a method that uses fuzzy logic to control systems rather than conventional binary logic. It allows for more flexibility and precision than traditional on-off controls by using fuzzy sets and fuzzy rules that account for intermediate values between fully true and fully false. Fuzzy control can be used in systems that are too complex for conventional controls or where measurements are unclear due to sensor limitations.
Fuzzy based approach for temporary objective identificationAlexander Decker
This document presents a new fuzzy-based approach for identifying the most effective temporary objective for an enterprise. The approach uses a fuzzy decision-making system to systematically analyze enterprise experts' opinions on various quantitative and qualitative factors that influence objective setting. It involves fuzzifying the factors, developing IF-THEN rules relating the factors to objectives, applying the rules via inference, and defuzzifying the results to determine the most important objective. A hypothetical example demonstrates how the approach can identify the optimal short-term objective for a business based on prevailing sales, costs, inventory levels, prices and competitive conditions.
Particle Swarm Optimization for Gene cluster IdentificationEditor IJCATR
The understanding of gene regulation is the most basic need for the classification of genes within a DNA. These genes
within the DNA are grouped together into clusters also known as Transcription Units. The genes are grouped into transcription units
for the purpose of construction and regulation of gene expression and synthesis of proteins. This knowledge further contributes as
essential information for the process of drug design and to determine the protein functions of newly sequenced genomes. It is possible
to use the diverse biological information across multiple genomes as an input to the classification problem. The purpose of this work is
to show that Particle Swarm Optimization may provide for more efficient classification as compared to other algorithms. To validate
the approach E.Coli complete genome is taken as the benchmark genome.
O documento descreve uma aplicação da lógica fuzzy e do raciocínio baseado em casos (CBR) para o controle de processos contínuos na indústria, como a indústria cimenteira. A inteligência artificial pode apoiar a tomada de decisões de forma mais simples e precisa do que outros métodos. O trabalho apresenta um sistema especialista baseado em CBR e lógica fuzzy para o controle de um processo termoquímico e discute os resultados comparados com operadores humanos.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
Performance Analysis of Various Activation Functions in Generalized MLP Archi...Waqas Tariq
This document compares the performance of various activation functions in multilayer perceptron (MLP) neural networks. It analyzes MLP architectures using different activation functions, including bi-polar sigmoid, uni-polar sigmoid, hyperbolic tangent, conic section, and radial basis functions. Based on experiments, hyperbolic tangent performed the best in terms of accuracy, requiring fewer iterations than other functions to solve nonlinear problems. While conic section had the lowest training error, hyperbolic tangent produced the most accurate results during testing. In general, the hyperbolic tangent function achieved high accuracy and is a good choice for most MLP applications.
Expert system design for elastic scattering neutrons optical model using bpnnijcsa
In present paper, a proposed expert system is designed to obtain a trained formulae for the optical model
parameters used in elastic scattering neutrons of light nuclei for (7Li), at energy range between [(1) to
(20)] MeV. A simple algorithm has used to design this expert system, while a multi-layer backwardpropagation
neural network (BPNN) is applied for training and testing the data used in this model. This
group of formulae may get a simple expert system occurring from governing formulae model, and predicts
the critical parameters usually resulted from the complicated computer coding methods. This expert system
may use in nuclear reactions yields in both fission and fusion nature who gives more closely results to the
real model.
Real-time traffic sign detection and recognition using Raspberry Pi IJECEIAES
This document presents a real-time traffic sign detection and recognition system developed using a Raspberry Pi 3 processor. The system uses a Raspberry Pi camera to record real-time video and the TensorFlow machine learning algorithm to detect and identify traffic signs based on a dataset of 500 labeled images across 5 sign classes. The system's accuracy, delay, and reliability were evaluated during testbed implementation considering different environmental and sign conditions. Results showed the system achieved over 90% accuracy on average with a maximum detection delay of 3.44 seconds, demonstrating reliable performance even for broken, faded, or low-light signs. This real-time traffic sign recognition system developed with affordable hardware has potential to increase road safety.
Performance analysis of real-time and general-purpose operating systems for p...IJECEIAES
In general, modern operating systems can be divided into two essential parts, real-time operating systems (RTOS) and general-purpose operating systems (GPOS). The main difference between GPOS and RTOS is the system is time-critical or not. It means that; in GPOS, a high-priority thread cannot preempt a kernel call. But, in RTOS, a low-priority task is preempted by a high-priority task if necessary, even if it’s executing a kernel call. Most Linux distributions can be used as both GPOS and RTOS with kernel modifications. In this study, two Linux distributions, Ubuntu and Pardus, were analyzed and their performances were compared both as GPOS and RTOS for path planning of the multi-robot systems. Robot groups with different numbers of members were used to perform the path tracking tasks using both Ubuntu and Pardus as GPOS and RTOS. In this way, both the performance of two different Linux distributions in robotic applications were observed and compared in two forms, GPOS, and RTOS.
Modeling and simulation of single phase transformer inrush current using neur...Alexander Decker
This document discusses modeling and simulating transformer inrush current using a neural network. It presents equations to calculate inrush current based on transformer parameters. Data on time and flux linkage values during inrush are obtained via simulation in MATLAB and used to train a neural network. The trained network accurately models the inrush current waveform, with an average 1.056% error compared to a semi-analytic solution. Tables and figures show the neural network structure, weights, training error reduction, and ability to model the magnetization curve matching the semi-analytic solution.
Using petri net with inherent fuzzy in the recognition of ecg signalsIAEME Publication
This document presents a method for classifying ECG signals using a fuzzy Petri net with inherent fuzziness. The fuzzy Petri net structure is organized into a neural network called a fuzzy Petri network. Features are extracted from ECG signals using wavelet transforms. A best basis technique is used to select optimal features that reduce the dimensionality of input vectors and complexity. The fuzzy Petri network parameters are learned using backpropagation. The system was tested on 8 classes of ECG beats and achieved accurate classification.
Incorporating Kalman Filter in the Optimization of Quantum Neural Network Par...Waqas Tariq
Kalman filter have been used for the estimation of instantaneous states of linear dynamic systems. It is a good tool for inferring of missing information from noisy measurement. The quantum neural network is another approach to the merging of fuzzy logic with the neural network and that by the investment of quantum mechanics theory in building the structure of neural network. The gradient descent algorithm has been used, widely, in training the neural network, but the problem of local minima is one of the disadvantages of this algorithm. This paper presents an algorithm to train the quantum neural network by using the extended kalman filter.
Nonlinear autoregressive moving average l2 model based adaptive control of no...Mustefa Jibril
This document discusses the use of neural network controllers for nonlinear systems based on nonlinear autoregressive moving average (NARMA) models. It specifically examines using a NARMA-L2 model to design three different neural network controllers for a nerves system based arm position sensor device: 1) a neural network controller with NARMA-L2 system identification, 2) a neural network controller with NARMA-L2 model predictive control, and 3) a neural network controller with NARMA-L2 model reference adaptive control. Simulation results show the neural network controller with NARMA-L2 model reference adaptive control had the best performance for controlling the arm position under different input signals.
High - Performance using Neural Networks in Direct Torque Control for Asynchr...IJECEIAES
This article investigates solution for the biggest problem of the Direct Torque Control on the asynchronous machine to have the high dynamic performance with very simple hysteresis control scheme. The Conventional Direct Torque Control (CDTC) suffers from some drawbacks such as high current, flux and torque ripple, as well as flux control at very low speed. In this paper, we propose an intelligent approach to improve the direct torque control of induction machine which is an artificial neural networks control. The principle, the numerical procedure and the performances of this method are presented. Simulations results show that the proposed ANN-DTC strategy effectively reduces the torque and flux ripples at low switching frequency, compared with Fuzzy Logic DTC and The Conventional DTC.
Black-box modeling of nonlinear system using evolutionary neural NARX modelIJECEIAES
Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system.
This document presents research on an adaptive vague controller based power system stabilizer (AVCPSS). It begins with an abstract that describes using an Adaptive Network Based Vague Inference System (ANVIS) to develop an Adaptive Vague Set Based Controller Power System Stabilizer (AVCPSS) capable of providing stabilization signals over a wide range of operating conditions and disturbances. Section I provides further introduction and background. Section II describes vague set theory and vague controllers, while Section III details the development of a Vague Set Based Controller Power System Stabilizer (VCPSS). Section IV introduces ANVIS for implementing learning and adaptation. Section V discusses the AVCPSS developed using ANVIS. Results in Section VI show the
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Comparison of backstepping, sliding mode and PID regulators for a voltage inv...IJECEIAES
In the present paper, an efficient and performant nonlinear regulator is designed for the control of the pulse width modulation (PWM) voltage inverter that can be used in a standalone photovoltaic microgrid. The main objective of our control is to produce a sinusoidal voltage output signal with amplitude and frequency that are fixed by the reference signal for different loads including linear or nonlinear types. A comparative performance study of controllers based on linear and non-linear techniques such as backstepping, sliding mode, and proportional integral derivative (PID) is developed to ensure the best choice among these three types of controllers. The performance of the system is investigated and compared under various operating conditions by simulations in the MATLAB/Simulink environment to demonstrate the effectiveness of the control methods. Our investigation shows that the backstepping controller can give better performance than the sliding mode and PID controllers. The accuracy and efficiency of the proposed backstepping controller are verified experimentally in terms of tracking objectives.
Comparison of cascade P-PI controller tuning methods for PMDC motor based on ...IJECEIAES
In this paper, there are two contributions: The first contribution is to design a robust cascade P-PI controller to control the speed and position of the permanent magnet DC motor (PMDC). The second contribution is to use three methods to tuning the parameter values for this cascade controller by making a comparison between them to obtain the best results to ensure accurate tracking trajectory on the axis to reach the desired position. These methods are the classical method (CM) and it requires some assumptions, the genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). The simulation results show the system becomes unstable after applying the load when using the classical method because it assumes cancellation of the load effect. Also, an overshoot of about 3.763% is observed, and a deviation from the desired position of about 12.03 degrees is observed when using the GA algorithm, while no deviation or overshoot is observed when using the PSO algorithm. Therefore, the PSO algorithm has superiority as compared to the other two methods in improving the performance of the PMDC motor by extracting the best parameters for the cascade P-PI controller to reach the desired position at a regular speed.
Available transfer capability computations in the indian southern e.h.v power...eSAT Journals
Abstract This paper presents three methods for computing the available transfer capability (ATC). One method is the conventional method known as continuation repeated power flow (CRPF) and other two are the intelligent techniques known as radial basis function neural network (RBFNN), basic adaptive neuro fuzzy inference system (ANFIS). In these two intelligent techniques, the basic ANFIS works with a multiple input single output (MISO) and it is modified as multiple input multiple output (MIMO) ANFIS using the proposed MIMO ANFIS. The main intension of this proposed method is to utilise the significant features of ANFIS with respect to the multiple outputs, as the basic ANFIS has been proved as the best intelligent techniques in the modelling of any application, but it has a disadvantage of single output and this drawback will be overcome using the proposed MIMO ANFIS. In this paper, the latest Indian southern region extra high voltage (SREHV) 72-bus system considered as test system to obtain the ATC computations using three methods. The ATC computations at desired buses are obtained with and without contingencies and compared the conventional ATC computations with the intelligent techniques. The obtained results are scrupulously verified with different test patterns and observed that the accuracy of proposed method is proved as the best as compared to the other methods for computing ATC. In this way, this paper shows a better way to compute ATC for the different open power market system Keywords— Available Transfer Capability, Total Transfer Capability, Open Power Markets, Repeated Power Flow, Radial Basis Function Neural Networks, Adaptive Neuro Fuzzy Inference System etc.
Fuzzy control is a method that uses fuzzy logic to control systems rather than conventional binary logic. It allows for more flexibility and precision than traditional on-off controls by using fuzzy sets and fuzzy rules that account for intermediate values between fully true and fully false. Fuzzy control can be used in systems that are too complex for conventional controls or where measurements are unclear due to sensor limitations.
Fuzzy based approach for temporary objective identificationAlexander Decker
This document presents a new fuzzy-based approach for identifying the most effective temporary objective for an enterprise. The approach uses a fuzzy decision-making system to systematically analyze enterprise experts' opinions on various quantitative and qualitative factors that influence objective setting. It involves fuzzifying the factors, developing IF-THEN rules relating the factors to objectives, applying the rules via inference, and defuzzifying the results to determine the most important objective. A hypothetical example demonstrates how the approach can identify the optimal short-term objective for a business based on prevailing sales, costs, inventory levels, prices and competitive conditions.
Particle Swarm Optimization for Gene cluster IdentificationEditor IJCATR
The understanding of gene regulation is the most basic need for the classification of genes within a DNA. These genes
within the DNA are grouped together into clusters also known as Transcription Units. The genes are grouped into transcription units
for the purpose of construction and regulation of gene expression and synthesis of proteins. This knowledge further contributes as
essential information for the process of drug design and to determine the protein functions of newly sequenced genomes. It is possible
to use the diverse biological information across multiple genomes as an input to the classification problem. The purpose of this work is
to show that Particle Swarm Optimization may provide for more efficient classification as compared to other algorithms. To validate
the approach E.Coli complete genome is taken as the benchmark genome.
O documento descreve uma aplicação da lógica fuzzy e do raciocínio baseado em casos (CBR) para o controle de processos contínuos na indústria, como a indústria cimenteira. A inteligência artificial pode apoiar a tomada de decisões de forma mais simples e precisa do que outros métodos. O trabalho apresenta um sistema especialista baseado em CBR e lógica fuzzy para o controle de um processo termoquímico e discute os resultados comparados com operadores humanos.
Fuzzy logic is a rule-based system that handles ambiguity and vagueness between two extremes. It allows systems to be defined using logic equations rather than complex math. The paper describes how a fuzzy logic system was used to control a solar tracking system. It discusses the history and key concepts of fuzzy logic, including membership functions, fuzzy rules and subsets, which allow systems to model real-world gray areas between black and white, true and false, definitions.
This document discusses fuzzy logic, beginning with its origins in ancient Greece and formalization in 1965 by Lotfi Zadeh. It explains fuzzy logic represents concepts with overlapping membership functions rather than binary logic. Fuzzy logic and neural networks both model human reasoning but fuzzy logic uses linguistic rules while neural networks learn from examples. Fuzzy logic has applications in control systems like temperature controllers and anti-lock braking systems to handle nonlinear dynamics. It is used in other fields like business and expert systems to represent subjective concepts.
On fuzzy concepts in engineering ppt. ncceSurender Singh
This document discusses fuzzy concepts and their applications in engineering. It begins with an introduction to fuzzy sets and fuzzy logic as extensions of crisp/classical sets and logic. Examples are provided to illustrate fuzzy membership functions. The key components of a fuzzy logic system are described. An example is given of building a fuzzy controller for room temperature regulation. Various engineering terms that are often used fuzzily, like bandwidth and errors, are listed. Applications of fuzzy logic in various fields like controls, scheduling, and signal analysis are outlined. Three probabilistic divergence measures and their fuzzy analogues are presented. Finally, a model for strategic decision making using these divergence measures is proposed and an illustrative example is provided.
In this paper, DTC is applied for two-level inverter fed IM drives based on neuronal hysteresis comparators and The Direct Torque Control (DTC) is known to produce quick and robust response in AC drive system. However, during steady state, torque, flux and current ripple. An improvement of electric drive system can be obtained using a DTC method based on ANNs which reduces the torque and flux ripples, the estimated the rotor speed using the KUBOTA observer method based on measurements of electrical quantities of the motor. The validity of the proposed methods is confirmed by the simulation results.The THD (Total Harmonic Distortion) of stator current, torque ripple and stator flux ripple are determined and compared with conventional DTC control scheme using Matlab/Simulink environment.
Modeling and Simulation of power system using SMIB with GA based TCSC controllerIOSR Journals
This document summarizes a study that uses genetic algorithms to tune a thyristor-controlled series compensator (TCSC) controller to improve the stability of a single-machine infinite-bus (SMIB) power system model. The study models the SMIB system and implements a TCSC to damp oscillations. Genetic algorithms are used to optimize the TCSC controller parameters. Simulation results show that the genetically-tuned TCSC controller more effectively damps oscillations compared to the system without a TCSC controller.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
LIGHTWEIGHT MOBILE WEB SERVICE PROVISIONING FOR THE INTERNET OF THINGS MEDIATIONijujournal
Emerging sensor-embedded smartphones motivated the mobile Internet of Things research. With the
integrated embedded hardware and software sensor components, and mobile network technologies,
smartphones are capable of providing various environmental context information via embedded mobile
device-hosted Web services (MWS). MWS enhances the capability of various mobile sensing applications
such as mobile crowdsensing, real time mobile health monitoring, mobile social network in proximity and
so on. Although recent smartphones are quite capable in terms of mobile data transmission speed and
computation power, the frequent usage of high performance multi-core mobile CPU and the high speed
3G/4G mobile Internet data transmission will quickly drain the battery power of the mobile device.
Although numerous previous researchers have tried to overcome the resource intensive issues in mobile
embedded service provisioning domain, most of the efforts were constrained because of the underlying
resource intensive technologies. This paper presents a lightweight mobile Web service provisioning
framework for mobile sensing which utilises the protocols that were designed for constrained Internet of
Things environment. The prototype experimental results show that the proposed framework can provide
higher throughput and less resource consumption than the traditional mobile Web service frameworks.
SPEED AND TORQUE CONTROL OF AN INDUCTION MOTOR WITH ANN BASED DTCijics
Due to advantages such as fast dynamic response, simple and robust control structure, direct torque
control (DTC) is commonly used method in high performance control method for induction motors. Despite
mentioned advantages, there are some chronically disadvantages with this method like high torque and
current ripples, variable switching behaviour and control problems at low speed rates. On the other hand,
artificial neural network (ANN) based control algorithms are getting increasingly popular in recent years
due to their positive contribution to the system performance. The purpose of this paper is investigating of
the effects of ANN integrated DTC method on induction motor performance by numerical simulations. For
this purpose, two different ANN models have been designed, trained and implemented for the same DTC
model. The first ANN model was designed to select optimum inverter and the second model was designed to
use in the determination of the flux vector position. Matlab/Simulink model of the proposed ANN based
DTC method was created in order to compare with the conventional DTC and the proposed DTC methods.
The simulation studies proved that the induction motor torque ripples have been reduced remarkably with
the proposed method and this approach can be a good alternative to the conventional DTC method for
induction motor control.
ARTIFICIAL NEURAL NETWORK BASED POWER SYSTEM STABILIZER ON A SINGLE MACHINE ...EEIJ journal
In this paper the use of artificial neural network in power system stability is studied. A predictive
controller based on two neural networks is designed and tested on a single machine infinite bus system
which is used to replace conventional power system stabilizers. They have been used for decades in
power system to dampen small amplitude low frequency oscillation in power systems. The increases in
size and complexity of power systems have cast a shadow on efficiency of conventional method. New
control strategies have been proposed in many researches. Artificial Neural Networks have been studied
in many publications but lack of assurance of their functionality has hindered the practical usage of them
in utilities. The proposed control structure is modelled using a novel data exchange established between
MATLAB and DIgSILENT power factory. The result of simulation proves the efficiency of the proposed
structure.
ARTIFICIAL NEURAL NETWORK BASED POWER SYSTEM STABILIZER ON A SINGLE MACHINE ...EEIJ journal
In this paper the use of artificial neural network in power system stability is studied. A predictive
controller based on two neural networks is designed and tested on a single machine infinite bus system
which is used to replace conventional power system stabilizers. They have been used for decades in
power system to dampen small amplitude low frequency oscillation in power systems. The increases in
size and complexity of power systems have cast a shadow on efficiency of conventional method. New
control strategies have been proposed in many researches. Artificial Neural Networks have been studied
in many publications but lack of assurance of their functionality has hindered the practical usage of them
in utilities. The proposed control structure is modelled using a novel data exchange established between
MATLAB and DIgSILENT power factory. The result of simulation proves the efficiency of the proposed
structure.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
The neural network-based control system of direct current motor driverIJECEIAES
This article aims to propose an adaptive control system for the direct current motor driver based on the neural network. The control system consists of two neural networks: the first neural network is used to estimate the speed of the direct current motor and the second neural network is used as a controller. The plant in this research includes motor and the driver circuit so it is a complex model. It is difficult to determine the exact parameters of the plant so it is difficult to build the controller. To solve the above difficulties, the author proposes an adaptive control system based on the neural network to control the plant reach the high quality in the case of unknowing the parameters of the plant. The results are that the control quality of the system is very good, the response speed always follows the desired speed and the transition time is small. The simulation results of the neural network control system are shown and compared with that of a PID controller to demonstrate the advantages of the proposed method.
A Novel Technique for Tuning PI-controller in Switched Reluctance Motor Drive...IJECEIAES
This paper presents, an optimal basic speed controller for switched reluctance motor (SRM) based on ant colony optimization (ACO) with the presence of good accuracies and performances. The control mechanism consists of proportional-integral (PI) speed controller in the outer loop and hysteresis current controller in the inner loop for the three phases, 6/4 switched reluctance motor. Because of nonlinear characteristics of a SRM, ACO algorithm is employed to tune coefficients of PI speed controller by minimizing the time domain objective function. Simulations of ACO based control of SRM are carried out using MATLAB /SIMULINK software. The behavior of the proposed ACO has been estimated with the classical Ziegler- Nichols (ZN) method in order to prove the proposed approach is able to improve the parameters of PI chosen by ZN method. Simulations results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on classical Ziegler-Nichols method.
IRJET- A Review on SVM based Induction MotorIRJET Journal
This document summarizes several research papers on using support vector machines (SVMs) and other machine learning techniques for fault detection in induction motors. Specifically:
1. It discusses using an artificial immune system-optimized SVM for detecting broken rotor bars and stator faults in induction motors based on motor current data.
2. It describes using wavelet analysis, principal component analysis, and SVM classification to detect faults like frequency variations, unbalanced voltages, and interturn shorts based on motor current spectra.
3. It proposes using dq0 voltage components analyzed with fast Fourier transforms as features for an SVM classifier to detect stator winding shorts, achieving over 98% accuracy.
TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithmpaperpublications3
Abstract: Flexible Alternating Current Transmission Systems (FACTS) devices represents a technological development in electrical power systems to have a tendency to generate the power with minimum price and less time that fulfill our requirement according to our need. Now a days Flexible AC Transmission System (FACTS) devices play a vital role in boost the power of system performance and power transfer capability. TCSC is an important member of family. In practical TCSC implementation, several such basic compensators may be connected in series to obtain the desired voltage rating and operating characteristics, so its placement is very important. This paper represent a meta heuristic hybrid Algorithm of Artificial Bee Colony (ABC) and Differential Evolution (DE) for finding the best placement and parameter setting of Thyristor Controlled Series capacitor to attain optimum power flow (OPF) of grid network. The proposed technique is tested at IEEE-30 bus test System. Result shows that the selected technique is one of the best for placement of TCSC for Secured optimum Power Flow (OPF).
Keywords: Optimal placement, Severity index, stressed power system, System loadability, TCSC, Hybrid DE/ABC.
Title: TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithm
Author: Ritesh Diwan, Preeti Sahu
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
This document describes a brain-computer interface system that uses steady-state visually evoked potentials detected by electroencephalography to control a drone. The system uses five flashing lights at different frequencies to elicit neural responses, which are classified using recursive least squares adaptive filtering and canonical correlation analysis to map the responses to commands to control drone movement. The system was able to successfully discriminate between the five frequencies and allow a user to control the drone within 5-10 seconds using their brain signals.
Comparison of dc motor speed control performance using fuzzy logic and model ...Mustefa Jibril
1) The document compares the performance of controlling the speed of a DC motor using fuzzy logic and model predictive control (MPC) methods.
2) Simulation results show that the DC motor controlled with a fuzzy logic controller had less overshoot and faster settling time when tracking speed setpoints compared to the motor controlled with an MPC controller.
3) For both square wave and white noise speed setpoints, the MPC controller resulted in higher overshoot and longer settling times than the fuzzy logic controller.
A novel efficient adaptive-neuro fuzzy inference system control based smart ...IJECEIAES
This document presents a novel adaptive-neuro fuzzy inference system (ANFIS) control algorithm for a smart grid integrating solar, wind, and grid power sources. The proposed ANFIS controller is used to improve the steady-state and transient response of the hybrid power system. Fuzzy logic maximum power point tracking algorithms are used to extract maximum power from solar photovoltaic panels and a permanent magnet synchronous generator is used for wind power generation. Back-to-back voltage source converters operated by the ANFIS controller are used to maximize both renewable power generations. Simulation results under different operating conditions and nonlinear faults show the proposed ANFIS control algorithm improves the overall system performance.
CONTROL OF AN INDUCTION MOTOR WITH DOUBLE ANN MODEL BASED DTCcsandit
Direct torque control (DTC) is preferably control method on high performance control of induction motors due to its dvantages such as fast dynamic response, simple and robust control structure. However, high torque and current ripples are mostly faced problems in this control method. This paper presents artificial neural network (ANN) based approach to the DTC method to overcome mentioned problems. In the study, by taking a different perspective to ANN and DTC integration, two different ANN models have been designed, trained and implemented. The first ANN model has been used for switch selecting process and the second one has been used for sector determine process. Matlab/Simulink model of the proposed ANN based DTC method has created in order to compare with the conventional DTC and the proposed DTC
methods. The simulation studies have proved that the induction motor torque and current
ripples have been reduced remarkably with the proposed method and this approach can be a
good alternative to the conventional DTC method for induction motor control
OPTIMAL TORQUE RIPPLE CONTROL OF ASYNCHRONOUS DRIVE USING INTELLIGENT CONTROL...elelijjournal
This document summarizes a research paper on optimizing torque ripple in an asynchronous drive using intelligent controllers. It describes using a hybrid controller combining PI and fuzzy logic control with direct torque control and space vector modulation of a three-level cascaded inverter to power an induction motor drive. Simulation results showed the hybrid controller approach minimized torque ripple compared to using just PI or fuzzy logic control alone under no-load and loaded conditions. The optimized torque control method provides benefits for general purpose induction motor applications.
Automatic Generation Control of Multi-Area Power System with Generating Rate ...IJAPEJOURNAL
In a large inter-connected system, large and small generating stations are synchronously connected and hence all stations must have the same frequency. The system frequency deviation is the sensitive indicator of real power imbalance. The main objectives of AGC are to maintain constant frequency and tie-line errors with in prescribed limit. This paper presents two new approaches for Automatic Generation Control using i) combined Fuzzy Logic and Artificial Neural Network Controller (FLANNC) and ii) Hybrid Neuro Fuzzy Controller (HNFC) with gauss membership functions. The simulation model is created for four-area interconnected power system. In this four area system, three areas consist of steam turbines and one area consists of hydro turbine. The components of ACE, frequency deviation (F) and tie line error (Ptie) are obtained through simulation model and used to produce the required control action to achieve AGC using i) FLANNC and ii) HNFC with gauss membership functions. The simulation results show that the proposed controllers overcome the drawbacks associated with conventional integral controller, Fuzzy Logic Controller (FLC), Artificial Neural Network controller (ANNC) and HNFC with gbell membership functionsv
In this paper a new combination Radial Basis Function Neural Network and p-q Power Theory (RBFNN-PQ) proposed to control shunt active power filters (SAPF). The recommended system has better specifications in comparison with other control methods. In the proposed combination an RBF neural network is employed to extract compensation reference current when supply voltages are distorted and/or unbalance sinusoidal. In order to make the employed model much simpler and tighter an adaptive algorithm for RBF network is proposed. The proposed RBFNN filtering algorithm is based on efficient training methods called hybrid learning method.The method requires a small size network, very robust, and the proposed algorithms are very effective. Extensive simulations are carried out with PI as well as RBFNN controller for p-q control strategies by considering different voltage conditions and adequate results were presented.
Analysis of intelligent system design by neuro adaptive control no restrictioniaemedu
This document discusses using neuro-adaptive control to analyze the design of intelligent systems. It begins by introducing the topic and noting that conventional adaptive control techniques assume explicit system models or dynamic structures based on linear models, which may not be valid for complex nonlinear systems. Neural networks and other intelligent control approaches that do not require explicit mathematical modeling are presented as alternatives. The paper then focuses on using time-delay neural networks for system identification and control of nonlinear dynamic systems. Various neural network architectures and learning algorithms for system modeling and control are described.
Abnormalities of hormones and inflammatory cytokines in women affected with p...Alexander Decker
Women with polycystic ovary syndrome (PCOS) have elevated levels of hormones like luteinizing hormone and testosterone, as well as higher levels of insulin and insulin resistance compared to healthy women. They also have increased levels of inflammatory markers like C-reactive protein, interleukin-6, and leptin. This study found these abnormalities in the hormones and inflammatory cytokines of women with PCOS ages 23-40, indicating that hormone imbalances associated with insulin resistance and elevated inflammatory markers may worsen infertility in women with PCOS.
A usability evaluation framework for b2 c e commerce websitesAlexander Decker
This document presents a framework for evaluating the usability of B2C e-commerce websites. It involves user testing methods like usability testing and interviews to identify usability problems in areas like navigation, design, purchasing processes, and customer service. The framework specifies goals for the evaluation, determines which website aspects to evaluate, and identifies target users. It then describes collecting data through user testing and analyzing the results to identify usability problems and suggest improvements.
A universal model for managing the marketing executives in nigerian banksAlexander Decker
This document discusses a study that aimed to synthesize motivation theories into a universal model for managing marketing executives in Nigerian banks. The study was guided by Maslow and McGregor's theories. A sample of 303 marketing executives was used. The results showed that managers will be most effective at motivating marketing executives if they consider individual needs and create challenging but attainable goals. The emerged model suggests managers should provide job satisfaction by tailoring assignments to abilities and monitoring performance with feedback. This addresses confusion faced by Nigerian bank managers in determining effective motivation strategies.
A unique common fixed point theorems in generalized dAlexander Decker
This document presents definitions and properties related to generalized D*-metric spaces and establishes some common fixed point theorems for contractive type mappings in these spaces. It begins by introducing D*-metric spaces and generalized D*-metric spaces, defines concepts like convergence and Cauchy sequences. It presents lemmas showing the uniqueness of limits in these spaces and the equivalence of different definitions of convergence. The goal of the paper is then stated as obtaining a unique common fixed point theorem for generalized D*-metric spaces.
A trends of salmonella and antibiotic resistanceAlexander Decker
This document provides a review of trends in Salmonella and antibiotic resistance. It begins with an introduction to Salmonella as a facultative anaerobe that causes nontyphoidal salmonellosis. The emergence of antimicrobial-resistant Salmonella is then discussed. The document proceeds to cover the historical perspective and classification of Salmonella, definitions of antimicrobials and antibiotic resistance, and mechanisms of antibiotic resistance in Salmonella including modification or destruction of antimicrobial agents, efflux pumps, modification of antibiotic targets, and decreased membrane permeability. Specific resistance mechanisms are discussed for several classes of antimicrobials.
A transformational generative approach towards understanding al-istifhamAlexander Decker
This document discusses a transformational-generative approach to understanding Al-Istifham, which refers to interrogative sentences in Arabic. It begins with an introduction to the origin and development of Arabic grammar. The paper then explains the theoretical framework of transformational-generative grammar that is used. Basic linguistic concepts and terms related to Arabic grammar are defined. The document analyzes how interrogative sentences in Arabic can be derived and transformed via tools from transformational-generative grammar, categorizing Al-Istifham into linguistic and literary questions.
A time series analysis of the determinants of savings in namibiaAlexander Decker
This document summarizes a study on the determinants of savings in Namibia from 1991 to 2012. It reviews previous literature on savings determinants in developing countries. The study uses time series analysis including unit root tests, cointegration, and error correction models to analyze the relationship between savings and variables like income, inflation, population growth, deposit rates, and financial deepening in Namibia. The results found inflation and income have a positive impact on savings, while population growth negatively impacts savings. Deposit rates and financial deepening were found to have no significant impact. The study reinforces previous work and emphasizes the importance of improving income levels to achieve higher savings rates in Namibia.
A therapy for physical and mental fitness of school childrenAlexander Decker
This document summarizes a study on the importance of exercise in maintaining physical and mental fitness for school children. It discusses how physical and mental fitness are developed through participation in regular physical exercises and cannot be achieved solely through classroom learning. The document outlines different types and components of fitness and argues that developing fitness should be a key objective of education systems. It recommends that schools ensure pupils engage in graded physical activities and exercises to support their overall development.
A theory of efficiency for managing the marketing executives in nigerian banksAlexander Decker
This document summarizes a study examining efficiency in managing marketing executives in Nigerian banks. The study was examined through the lenses of Kaizen theory (continuous improvement) and efficiency theory. A survey of 303 marketing executives from Nigerian banks found that management plays a key role in identifying and implementing efficiency improvements. The document recommends adopting a "3H grand strategy" to improve the heads, hearts, and hands of management and marketing executives by enhancing their knowledge, attitudes, and tools.
This document discusses evaluating the link budget for effective 900MHz GSM communication. It describes the basic parameters needed for a high-level link budget calculation, including transmitter power, antenna gains, path loss, and propagation models. Common propagation models for 900MHz that are described include Okumura model for urban areas and Hata model for urban, suburban, and open areas. Rain attenuation is also incorporated using the updated ITU model to improve communication during rainfall.
A synthetic review of contraceptive supplies in punjabAlexander Decker
This document discusses contraceptive use in Punjab, Pakistan. It begins by providing background on the benefits of family planning and contraceptive use for maternal and child health. It then analyzes contraceptive commodity data from Punjab, finding that use is still low despite efforts to improve access. The document concludes by emphasizing the need for strategies to bridge gaps and meet the unmet need for effective and affordable contraceptive methods and supplies in Punjab in order to improve health outcomes.
A synthesis of taylor’s and fayol’s management approaches for managing market...Alexander Decker
1) The document discusses synthesizing Taylor's scientific management approach and Fayol's process management approach to identify an effective way to manage marketing executives in Nigerian banks.
2) It reviews Taylor's emphasis on efficiency and breaking tasks into small parts, and Fayol's focus on developing general management principles.
3) The study administered a survey to 303 marketing executives in Nigerian banks to test if combining elements of Taylor and Fayol's approaches would help manage their performance through clear roles, accountability, and motivation. Statistical analysis supported combining the two approaches.
A survey paper on sequence pattern mining with incrementalAlexander Decker
This document summarizes four algorithms for sequential pattern mining: GSP, ISM, FreeSpan, and PrefixSpan. GSP is an Apriori-based algorithm that incorporates time constraints. ISM extends SPADE to incrementally update patterns after database changes. FreeSpan uses frequent items to recursively project databases and grow subsequences. PrefixSpan also uses projection but claims to not require candidate generation. It recursively projects databases based on short prefix patterns. The document concludes by stating the goal was to find an efficient scheme for extracting sequential patterns from transactional datasets.
A survey on live virtual machine migrations and its techniquesAlexander Decker
This document summarizes several techniques for live virtual machine migration in cloud computing. It discusses works that have proposed affinity-aware migration models to improve resource utilization, energy efficient migration approaches using storage migration and live VM migration, and a dynamic consolidation technique using migration control to avoid unnecessary migrations. The document also summarizes works that have designed methods to minimize migration downtime and network traffic, proposed a resource reservation framework for efficient migration of multiple VMs, and addressed real-time issues in live migration. Finally, it provides a table summarizing the techniques, tools used, and potential future work or gaps identified for each discussed work.
A survey on data mining and analysis in hadoop and mongo dbAlexander Decker
This document discusses data mining of big data using Hadoop and MongoDB. It provides an overview of Hadoop and MongoDB and their uses in big data analysis. Specifically, it proposes using Hadoop for distributed processing and MongoDB for data storage and input. The document reviews several related works that discuss big data analysis using these tools, as well as their capabilities for scalable data storage and mining. It aims to improve computational time and fault tolerance for big data analysis by mining data stored in Hadoop using MongoDB and MapReduce.
1. The document discusses several challenges for integrating media with cloud computing including media content convergence, scalability and expandability, finding appropriate applications, and reliability.
2. Media content convergence challenges include dealing with the heterogeneity of media types, services, networks, devices, and quality of service requirements as well as integrating technologies used by media providers and consumers.
3. Scalability and expandability challenges involve adapting to the increasing volume of media content and being able to support new media formats and outlets over time.
This document surveys trust architectures that leverage provenance in wireless sensor networks. It begins with background on provenance, which refers to the documented history or derivation of data. Provenance can be used to assess trust by providing metadata about how data was processed. The document then discusses challenges for using provenance to establish trust in wireless sensor networks, which have constraints on energy and computation. Finally, it provides background on trust, which is the subjective probability that a node will behave dependably. Trust architectures need to be lightweight to account for the constraints of wireless sensor networks.
This document discusses private equity investments in Kenya. It provides background on private equity and discusses trends in various regions. The objectives of the study discussed are to establish the extent of private equity adoption in Kenya, identify common forms of private equity utilized, and determine typical exit strategies. Private equity can involve venture capital, leveraged buyouts, or mezzanine financing. Exits allow recycling of capital into new opportunities. The document provides context on private equity globally and in developing markets like Africa to frame the goals of the study.
This document discusses a study that analyzes the financial health of the Indian logistics industry from 2005-2012 using Altman's Z-score model. The study finds that the average Z-score for selected logistics firms was in the healthy to very healthy range during the study period. The average Z-score increased from 2006 to 2010 when the Indian economy was hit by the global recession, indicating the overall performance of the Indian logistics industry was good. The document reviews previous literature on measuring financial performance and distress using ratios and Z-scores, and outlines the objectives and methodology used in the current study.
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
1.meena tushir finalpaper-1-12
1. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol 2, No 7, 2011
Design and Simulation of a Novel Clustering based Fuzzy
Controller for DC Motor Speed Control
Tushir Meena
Maharaja Surajmal Institute of Technology
New Delhi, India
Tel: +91-9811705113 E-mail: meenatushir@yahoo.com
Srivastava Smriti
Netaji Subhas Institute of Technology
New Delhi, India
Tel: +91-11-25500381 E-mail: ssmriti@yahoo.com
Received: October 21, 2011
Accepted: October 29, 2011
Published: November 4, 2011
Abstract
This research article proposes the speed control of a DC Motor (series as well as shunt motor). The novelty
of this article lies in the application of kernel based hybrid c-means clustering (KPFCM) in the design of
fuzzy controller for the speed control of DC Motor. The proposed approach provides a mechanism to obtain
the reduced rule set covering the whole input/output space as well as the parameters of membership
functions for each input variable. The performance of the proposed clustering based fuzzy logic controller
is compared with that of its corresponding conventional fuzzy logic controller in terms of several
performance measures such as rise time, peak overshoot, settling time, integral absolute error (IAE) and
integral of time multiplied absolute error (ITAE) and in each case, the proposed scheme shows improved
performance over its conventional counterpart. Also it shows that the proposed controller scheme gives
much faster results as it reduces the computational time.
Keywords: DC Motor, Fuzzy control, Kernel, Clustering, Validity index
1. Introduction
Inspite of the development of power electronics resources, the direct current machines are becoming more
and more useful in so far as they have found wide applications i.e. automobiles industry (electric vehicle),
the electric traction in the multi-machine systems etc. The speed of DC motor can be adjusted to a great
extent so as to provide easy control and high performance. There are several conventional and numeric
controllers intended for controlling the DC motor speed: PID controllers, fuzzy logic controllers; or the
combination between them, fuzzy neural networks etc. The nonlinearity of the series/shunt-connected
motors complicates their use in applications that require automatic speed control. Major problems in
applying a conventional control (Liu et. al 1999) algorithm in a speed controller are the effects of
non-linearity in a DC motor. One of intelligent technique, fuzzy logic by Zadeh is applied for controller
design in many applications. The advantage of fuzzy control methods is the fact that they are not sensitive
to the accuracy of the dynamical model. In motion control systems, fuzzy logic can be considered as an
alternative approach to conventional feedback control. It has been demonstrated in the literature that
dynamic performance of electric drives as well as robustness with regard to parameter variations can be
1|Page
www.iiste.org
2. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol 2, No 7, 2011
improved by adopting the non-linear speed control techniques. Fuzzy control is a non-linear control and it
allows the design of optimized non-linear controller to improve the dynamic performance of conventional
regulators. Several works are reported in literature (Iracleous and Alexandris 1995; B. Singh et. al 2000;
Montiel et. al 2007) where conventional controller is combined with the fuzzy controller to improve the
response of the DC motor under non-linearity, load disturbances, parameter variations etc.
From the application of fuzzy control arise two problems: how to select the fuzzy control rules and how to
set the membership functions. Two approaches are normally used to accomplish this task. One consists of
acquiring knowledge directly from skilled operators and translates it into fuzzy rules. This process, however,
can be difficult to implement and time consuming. As an alternative, fuzzy rules can be obtained through
machine learning techniques, where the knowledge of the process is automatically extracted or induced
from sample cases or examples. Many machine learning methods developed for building crisp logic
systems can be extended to learn fuzzy rules. A very popular machine learning method is artificial neural
network (ANN), which has been developed to mimic biological neural system in performing learning
control and pattern recognition. The application of hybrid fuzzy logic and NN has been presented in a
number of papers (Rubaai et.al 2000, Horng 2002).They are mainly focused either on the translation of
fuzzy reasoning or on the introduction of fuzzy concepts into NN. As the system is constructed, a fuzzy
logic controller controls the speed of DC motor and learning from a NN, which is used to implement the
input-output relationships of FLC without reproducing the fuzzy reasoning.
Clustering techniques have been recognized as a powerful alternative approach to develop fuzzy systems.
The purpose of clustering is to identify natural grouping of data from a large set to produce a concise
representation of a system’s behavior. Each cluster essentially identifies a region in the data space that
contains a sufficient mass of data to support the existence of a fuzzy input/output relationship. Because a
rule is generated only when there is a cluster of data, the resultant rules are scattered in the input space
rather than placed according to grid-like partitions in the input space. This fundamental feature of
clustering–based rule extraction methods helps avoid combinational explosion of rules with increasing
dimension of the input space. Also, because clustering step provides good initial rule parameter values, the
subsequent rule parameter optimization process usually converges quickly and to a good solution. The
fuzzy c-means clustering algorithm (Bezdek 1981) is one well known example of such clustering algorithm.
Recently, kernel methods for clustering attract more and more attentions. The kernel-based methods are
algorithms that, by replacing the inner product with an appropriate positive definite function, implicitly
perform a nonlinear mapping of the input data to a high dimensional feature space. Several studies on
kernel-based clustering (Zhang and Chen 2002; Camastra and Verri 2005) indicate that kernel clustering
algorithms are more accurate and more robust than the conventional clustering algorithms. Clustering
algorithms typically require the user to pre specify the number of cluster centers and their initial locations.
Different researchers have used different validity indices to decide the number of clusters. Our clustering
method called kernel based hybrid c-means clustering (KPFCM) (Tushir and Srivastava 2010) with a
kernelized Xie-beni validity index (Yuhua and Hall 2006) forms the basis of the present work. The clusters
are determined where each cluster center belongs to one rule in fuzzy logic. If there are more rules in Fuzzy
rule base, there is a drawback of more rule firing and high computational time. To avoid that, it is proposed
to eliminate rule redundancy through fuzzy clustering for parameter identification.
2. Dynamic Modeling of DC Motor
Historically, DC machines are classified according to the connection of the field circuit with respect to the
armature circuit. In shunt machines, the field circuit is connected in parallel with the armature circuit while
DC series machines have the field circuit in series with armature where both field and armature currents are
identical.
2.1 DC Series Motor
As its name indicates, the field circuit is connected in series with the armature and therefore the armature
and field currents are the same. The equivalent circuit of a DC series motor is shown in Figure 1a.
The equation of the armature circuit is:
2|Page
www.iiste.org
3. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol 2, No 7, 2011
( La LF )(dia / dt ) V ( Ra RF )ia K b (1)
The motion equation is:
J (d / dt ) K tia TL (2)
2.2 DC Shunt Motor
In shunt machine, the field circuit is connected in parallel with the armature circuit. It has the following
equivalent circuit (Figure 1b).
The mathematical model of the electromechanical system is as follows:
d / dt (3)
J (d / dt ) Tm B TL (4)
LF (diF / dt ) ( Radj RF )iF V (5)
Where
Tm K mia , Tm = Electromagnetic torque, K F iF , = Flux in armature
ia (V E A ) / Ra , ia = Armature current, Eb K b , Eb = Back Emf
TL = Load torque, V = Terminal voltage, LF = Field Inductance, J Rotor moment of inertia
La = Armature inductance, B = Viscous friction co-efficient, = Angular position, = speed
3. Clustering Analysis
Cluster analysis divides data into groups such as similar data objects belong to the same cluster and
dissimilar data objects to different clusters. The resulting data partition improves data understanding and
reveals its internal structure. Kernel methods (Christianini and Taylor 2000; Girolami 2002) have been
successfully applied in solving various problems in machine learning community. A kernel function is a
generalization of the distance metric that measures the distance between two data points as the data points
are mapped into a high dimensional space in which they are more clearly separable. By employing a
mapping function (x), which defines a non–linear transformation: x (x), the non-linearly
separable data structure existing in the original data space can possibly be mapped into a linearly separable
case in the higher dimensional feature space. The determination of the optimum number of the clusters is
the most important problem in the cluster analysis. In the present work, a reformulated kernelized Xie-Beni
validity index is used to determine the optimum number of clusters.
3.1 Kernel-Based Hybrid c-means Clustering Method and Corresponding Partition Validity
Our model called Kernel-based hybrid c-means clustering (KPFCM) adopts a kernel-induced metric
different from the Euclidean norm in original possibilistic fuzzy c-means clustering by Pal et. al (2005).
KPFCM minimizes theN c following objective function: c N
J KPFCM (U ,V , T ) (auik btik ) ( xk ) (vi ) i (1 tik )
m 2
k 1 i 1 i 1 k 1
(6)
where, ( xk ) (vi )
2
is the square of distance between ( xk ) and (vi ).
3|Page
www.iiste.org
4. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol 2, No 7, 2011
X {x1 , x2 ....., xN } is a set of vectors in an n-dimensional feature space, V (v1 , v2 ....., vc ) is a
c-tuple of prototypes, N is the total number of feature vectors, c is the number of clusters, u ik is the grade
of membership of feature point x k in cluster vi and m [1, ) is a weighting exponent called fuzzifier,
a possibilistic ( tik ) membership that measures the absolute degree of typicality of a point in any particular
cluster Here a>0, b>0, m>1 and >1. The constants a and b defines the relative importance of fuzzy
membership and typicality values in the objective function.
The distance in the feature space is calculated through the kernel in the input space as follows:
2
( xk ) (vi ) (( xk ) (vi )).(( xk ) (vi ))
= ( xk ).( xk ) 2( xk )(vi ) (vi )(vi )
K ( xk , xk ) 2K ( xk , vi ) K (vi , vi )
If we adopt the Gaussian function as a kernel function i.e. K ( x, y ) exp( x y / 2 ) , where
2 2
N
positive number, then K ( x, x) 1 .cThus N can be rewritten as
defined as kernel width, is a c (6)
J KPFCM (U ,V , T ) 2 (auik btik )(1 K ( xk , vi )) i (1 tik )
m
k 1 i 1 i 1 k 1
(7)
Given a set of points X, we minimize J KPFCM (U ,V , T ) in order to determine U, V, T.
c
uik (1/(1 K ( xk , vi )))1 / m1 / (1/(1 K ( xk , v j )))1 / m1 (8)
j 1
tik 1 / 1 ((2b(1 K ( xk , vi )) / i )1/( 1) (9)
N N
vi (auik btik ) K ( xk , vi ) xk / (auik btik ) K ( xk , vi )
m m
(10)
k 1 k 1
It is suggested to select i as
N N
i H (2 uik (1 K ( xk , vi ))) / ( uik )
m m
k 1 k 1
(11)
Typically, H is chosen as 1.
Xie and Beni (1991) proposed a fuzzy clustering validity function which measures compactness and
separation of fuzzy clustering. The kernelized Xie –beni validity metric can be reformulated as follows:
KXB (V , X ) k 1 i 1 uik ( xk ) (vi ) /( n min i , j (vi ) (v j )
m n c 2 2
N c
uik [2 2 * K ( xk , vi )] /( n min i , j [2 2 * K (vi , v j )])
m
k 1 i 1
) (12)
Using (8), (12) can be rewritten as:
N c c
([1 K ( xk , vi )] 1 /( m1)
/ [1 K ( xk , vi )]1/( m1) ) m * [2 2 * k ( xk , vi )] /( n min i , j [2 2 * K (vi , v j )])
k 1 i 1 j 1
4|Page
www.iiste.org
5. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol 2, No 7, 2011
1
2 * k 1 ( j 1[1 K ( xk , v j )] m 1 1 m
N c
) /( n * min i , j 2 * [1 K (vi , v j )]) (13)
The number of clusters is determined so that the smaller KXB means a more compact and separate
clustering. The goal should therefore be to minimize the value of KXB .
4. Controller Structure
Unlike conventional control, which is based on mathematical model of a plant, a fuzzy logic controller
(FLC) usually embeds the intuition and experience of a human operator and sometimes those of designers
and researchers. While controlling a plant, a skilled human operator manipulates the process input (i.e.
controller output) based on e and e with a view of minimizing the error within shortest possible time.
The controlled variable of fuzzy controller is u(t). In this paper, all membership functions for the
conventional fuzzy controller inputs ( e and e ) and the controller output are defined on the common
normalized domain [-1, 1]. We use symmetric triangles (except the two MFs at the extreme ends) with
equal base and 50% overlap with neighboring MFs. Here, the seven membership functions are shown in
Figure 3. The next step is to design the rule base. If the number of MFs for inputs is 7, the corresponding
rules are 72 =49 (Table 1). The objective here is to justify whether the system after the proposed clustering
(with less number of rules) can provide the same level of performance as that of original one (system with
49 rules). For the identification of the proposed clustering based controller, we used system identification
technique through exploratory data analysis where the controller outputs for different e and e are
available. Here we can generate the data by sampling input variables ( e and e ) uniformly and
computing the value of u for each sampled point. To extract the minimum rules, KPFCM clustering is
performed in the input space of each class of data. The clusters found in the data of a given group identify
regions in the input space that map into the associated class. Hence, each cluster center may be translated
into a fuzzy rule for identifying the class. Each rule has the following form:
Rule i: If x1 is Ai1 and …. and xn is Ain THEN yi ci 0 ci1 ..... cin xn (14)
Where i 1,2,....., l , l the number of IF-THEN rules, cik ' s(k 0,1,...., n) are the consequent parameters.
yi is an output from the i th IF –THEN rule, and Aij is the membership function (Gaussian type) in the
i th rule associated with the j th input feature.
The membership function Aij is given by
Aij ( x j ) exp( 0.5 * (( x j aij ) / ij ) 2 ) (15)
with a ij being the center and ij , the variance of the Gaussian curve.
There are several approaches to optimize the parameters. One approach is to apply a gradient descent
method to optimize the parameters aij , ij , cij in (14) and (15) to reduce the root-mean-square output
error with respect to the training data. Another approach also involves using the Takagi-Sugeno rule format,
but it combines optimizing the premise membership functions by back propagation with optimizing the
consequent equations by linear least squares estimation. This is the ANFIS methodology developed by Jang
(1993). We have used this approach in the paper.
5. Simulation and Analysis
In this section, we show the simulation results for speed control of DC motor using the proposed clustering
based controller and the conventional controller. The Conventional Fuzzy controller uses 49 rules and 7
membership functions to compute output. Now our main aim is to extract a smaller set of rules using
kernel-based hybrid c-means clustering. Here, KPFCM clustering with kernelized Xie-beni index is used to
determine the number of rules. Thus four clusters (rules) are extracted. After the number of rules is
determined, a fuzzy controller based on TS model using Gaussian membership function is designed with
KPFCM clustering providing with an initial guess for the parameters of the antecedent membership
functions i.e. the center and the width of the Gaussian membership function. The output response using
5|Page
www.iiste.org
6. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol 2, No 7, 2011
these rules may not be closed to the desired response. ANFIS is used for training to modify the above
parameters. Figure 3 shows the initial and final membership functions of the input variables ( e and e ).
Then, the performance of the proposed controller (identified System) is compared with the conventional
one.
5.1.1 DC Series Motor
Simulation experiments under different operation status are carried out based on the fore established model.
The simulink model for the DC series motor is shown in Figure 4. The parameters used for DC series motor
can be taken reference for simulation, as shown in appendix A. The two curves in Figure 5a are the
simulation curves of the rated running state for DC series motor respectively under the control of
conventional fuzzy controller and the proposed controller. For a clear comparison between the conventional
fuzzy controller and the proposed clustering based fuzzy controller, several performance measures such as
peak overshoot (%Mp), settling time, rise time, integral absolute error (IAE) and
integral-of-time-multiplied-absolute error (ITAE) are computed as shown in table 2. Using proposed
controller, the rise time and settling time improves whereas for other measures, both the controllers give
approximately the same performance. However, under load disturbance, the performance of the proposed
scheme shows improved results. Figure 5b shows the response of the system with a 40 % load disturbance
applied at t=5 secs. Table 3 shows the values of peak overshoot, settling time, IAE and ITAE computed
under this condition. After 5 seconds, the rated speed of the DC series motor is suddenly increased and then
decreased by 20% as shown in Figure 6 and Figure 7. As can be seen, under the condition of given speed
changing, the proposed clustering based controller compared with conventional fuzzy controller, is able to
quickly reaches a steady state and has better tracking performance.
5.2.2 DC Shunt Motor
In this section, DC shunt motor is used in simulation. Figure 8 shows the simulink model of DC shunt
motor. The machine parameters are given in appendix A. Figure 9a shows the simulation results of the two
controllers when the external load disturbance is zero. The performance of the two controllers is listed in
table 4. At the time t=15 secs, the external load torque is decreased by a step of 40 % (Figure 9b).The
system again reaches the steady state after transient period. Table 5 shows the values of peak overshoot,
settling time, IAE and ITAE computed under this condition. The illustrated figures verify that a significant
improvement has been achieved using the proposed clustering based controller. Initially the motor is
operated at the steady state. At the time t=20 secs, an increased step of 20 % of initial set point occurs
and then a decreased step of 20 % of initial set point as shown in Figure 10 and Figure 11, the rotor speed
tracks the new set point after a transient period. Obviously, the external load torque is assumed constant.
Comparisons with the conventional fuzzy controller indicate the improvement achieved. After reducing the
number of rules using clustering, the computation becomes fast. The computation time is measured and the
results are shown in table 6. Thus it can be seen that the proposed controller performs better than the
conventional fuzzy controller.
6. Conclusion
The proposed approach is used to extract the minimum number of rules from the given input-output data.
The proposed approach is able to reduce the number of rules from 49 to 4 rules giving improved level of
performance. The proposed scheme was applied to the speed control of DC series as well as DC shunt
motor. Performance of the proposed clustering based FLC was also compared with corresponding
conventional FLC’s with respect to several indices such as peak overshoot (%Mp), settling time, rise time,
Integral of absolute error (IAE) and integral-of-time-multiplied absolute error (ITAE) and from the
simulation, it shows that the proposed controller can track the reference speed satisfactorily even under load
torque disturbances. Another advantage of the proposed method is the reduced computational time as the
number of rules decreases from 49 rules to 4 rules. It can thus be concluded that proposed control scheme
can be successfully applied to the problem of designing a robust control for the DC motor system.
6|Page
www.iiste.org
7. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol 2, No 7, 2011
Appendix A
DC Series Motor parameters:
LF =44mH, RF=0.2 ohms, V =125V, La =18mH, Ra =0.24 ohms, J = 0.5kgm2, Kb=0.55, Kt=3
DC Shunt Motor parameters:
La=18 mH, Ra=0.24 ohm, Kt=Km=1000, J=0.5 kgm2, Kb =0.7, Lf=10H, Rf=120 ohms, Kf=Kb1=0.05
References
Liu Z.Z., Luo F.L., Rashid M.H. (1999), “Non-linear Speed Controllers for Series DC Motor”, Proc. of
IEEE Intl. Conf. on Power Electronics and Drive Systems, 1, 333-338.
Iracleous D.P., Alexandris A.T. (1995), “Fuzzy Tuned PI controller for Series Connected DC Motor Drives”,
Proc. of IEEE Symosium on Industrial Electronics, (2) 495-499.
Singh B., Reddy A.H.N., Murthy S.S. (2000), “Hybrid Fuzzy Logic Proportional plus Conventional
Integral-Derivative Controller for Permanent Magnet Brushless DC motor”, Proc. of IEEE Intl. conf. on
Industrial Technology, (1) 185-191.
Montiel Oscar et. al. (2007), “Performance of a Simple Tuned Fuzzy controller and a PID Controller on a
DC Motor”, Proc. of IEEE Symposium on Foundation of Computational Intelligence, 531-537.
Rubaai A., Ricketts D., Kankam M.D. (2000), “Development and Implementation of an Adaptive
Fuzzy-Neural-Network Controller for Brushless Drives”, Proc. of IEEE Intl. Conf. on Ind. Appl., 2(2)
947-953.
Jui-Hong Horng (2002), “SCADA System of DC Motor with Implementation of Fuzzy Logic Controller on
Neural Network”, Journal of Advances in Engineering Software, 33, 361-364.
Bezdek J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York, 1981.
Camastra Tu F., Verri A. (2005), “A Novel Kernel Method for Clustering”, IEEE Transaction on Pattern
Analysis and Machine Intelligence, 27 (5) 801-805.
Zhang D.Q., Chen S.C. (2002), “Fuzzy Clustering using Kernel Methods”, Proc. of Intl. Conf. on Control
and Automation, China 123-128.
Tushir M., Srivastava S. (2010), “A New Kernelized Hybrid c-means Clustering Model with Optimized
Parameters”, J. Applied Soft computing, 10 (2) 381-389.
Xie X.L., Beni G.A. (1991), “Validity measure for Fuzzy clustering”, IEEE Trans. Pattern Anal. Machine
Intell., 3, 841-846.
Yuhua G., Hall L.O. (2006), “Kernel based Fuzzy Ant Clustering with Partition Validity”, Proc. of IEEE Intl.
Conf. on fuzzy systems, 61-65.
Christianini N., Taylor J.S. (2000), An Introduction to SVMs and other Kernel-based Learning Methods,
Cambridge Univ. Press.
Girolami M. (2002), “Mercer Kernel-based Clustering in Feature Space”, IEEE Trans. on Neural Networks,
13 (3) 780-784.
Jang J.S.R. (1993), “ANFIS: Adaptive-Network-based Fuzzy Inference System”, IEEE Trans. Syst., Man,
Cybern. 23, 665-685.
Pal N.R., Pal K., Keller J., Bezdek J.C. (2005), “A Possibilistic Fuzzy c-means Clustering Algorithm”,
IEEE Trans. Fuzzy Syst. 13 (4) 517–530.
7|Page
www.iiste.org
8. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol 2, No 7, 2011
(a) (b)
Figure 1 Equivalent Circuit of (a) DC Series Motor (b) DC Shunt Motor
Figure 2 MFs for e , e and u
NB: Negative Big, NM: Negative Medium, NS: Negative Small, Z: Zero; PS: Positive
Small, PM: Positive Medium, PB: Positive Big
8|Page
www.iiste.org
9. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol 2, No 7, 2011
Figure 3 Initial and Final membership functions of e and Δe
Figure 4 Simulink Model of DC series motor
(a)
1.4
1.2
1
Speed (in p.u.)
0.8 W ithout Cluste ring
W ith Cluste ring
Re fe re nce
0.6
0.4
0.2
0
0 1 2 3 4 5 6 7 8 9 10
Time (se cs)
(b)
1.4
1.2
1
Speed(in p.u.)
0.8
Without Cluste ring
0.6 With Cluste ring
Re fe re nce
0.4
0.2
0
0 1 2 3 4 5 6 7 8 9 10
Time (se cs)
Figure 5 Speed response of DC series motor (a) without load disturbance (b) with load disturbance
1.4
1.2
1
Speed (in p.u.)
0.8
W ithout Cluste ring
W ith Cluste ring
0.6 Re fe re nce
0.4
0.2
0
0 1 2 3 4 5 6 7 8 9 10
Time (se cs)
9|Page
www.iiste.org
10. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol 2, No 7, 2011
Figure 6 Speed response of DC series motor with sudden increase in speed
1.4
1.2
1
Speed (in p.u.) 0.8
0.6
W ithout Clus te ring
W ith Clus te ring
0.4 Re fe re nce
0.2
0
0 1 2 3 4 5 6 7 8 9 10
Time (s cs
e )
Figure 7 Speed response of DC series motor with sudden decrease in speed
Figure 8 Simulink model of DC Shunt Motor
(a)
1.4
1.2
1
Speed(in p.u.)
0.8
W ithout Clus te ring
0.6 W ith clus te ring
re fe re nce s pe e d
0.4
0.2
0
0 5 10 15
Time (s cs
e )
(b)
1.4
1.2
1
Speed (in p.u.)
0.8
0.6
W ith Clustering
W ithout Clustering
0.4 Reference Speed
0.2
0
0 5 10 15 20 25 30 35 40
Time (se cs)
Figure 9 Speed response of DC Shunt motor (a) without load disturbance (b) with load disturbance
10 | P a g e
www.iiste.org
11. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol 2, No 7, 2011
1.4
1.2
1
Speed(in p.u.)
0.8
0.6
W ith Cluste rting
W ithout Cluste ring
0.4
0.2
0
0 5 10 15 20 25 30 35 40
time (se cs)
Figure 10 Speed response of DC Shunt motor with sudden increase in speed
1.4
1.2
1
Speed(in p.u.)
0.8
0.6
W ith Cluste ring
W ithout Cluste ring
0.4
0.2
0
0 5 10 15 20 25 30 35 40
Time (se cs)
Figure 11 Speed response of DC Shunt motor with sudden decrease in speed
Table 1
Rule Base
Δei / ei NB NM NS ZE PS PM PB
NB NB NB NB NM NS NS ZE
NM NB NM NM NM NS ZE PS
NS NB NM NS NS ZE PS PM
ZE NB NM NS ZE PS PM PB
PS NM NS ZE PS PS PM PB
PM NS ZE PS PM PM PM PB
PB ZE PS PS PM PB PB PB
Table 2
Numerical results of experiment on DC series motor without load disturbance
tr %Mp ts IAE ITAE
(secs) (secs)
Conventional 0.25 9 1.58 0.22 0.082
FLC
Proposed 0.15 10 1.3 0.23 0.086
11 | P a g e
www.iiste.org
12. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol 2, No 7, 2011
Controller
Table 3
Numerical results of experiment on DC series motor with load disturbance
%Mp ts IAE ITAE
(secs)
Conventional FLC 11.4 5.72 0.271 0.363
Proposed Controller 7 5.5 0.26 0.242
Table 4
Numerical results of experiment on DC shunt motor without load disturbance
tr %Mp ts IAE ITAE
(secs) (secs)
Conventional 0.79 9 3.03 0.588 0.432
FLC
Proposed 0.74 3 1.86 0.445 0.17
Controller
Table 5
Numerical results of experiment on DC shunt motor with load disturbance
%Mp ts IAE ITAE
Conventional FLC 26 16.78 0.823 4.14
Proposed Controller 16 16.1 0.53 1.83
Table 6
Computational Time of experiment on DC motor with conventional fuzzy controller and the proposed
clustering based controller
DC Series Motor DC Shunt Motor
Conventional FLC Proposed Controller Conventional FLC Proposed Controller
Without Load disturbance 4.38 secs 0.625 secs 6.12 secs 0.296 secs
With Load disturbance 3.96 secs 0.545 secs 9.17 secs 0.34 secs
With sudden Increase in 6.08 secs 0.286 secs 8.48 secs 1.35 secs
Reference Speed
With sudden Decrease in 4.21 secs 0.302 secs 7.29 secs 0.268 secs
Reference Speed
12 | P a g e
www.iiste.org