This paper presents a new methodology for designing a neuro-fuzzy control for complex physical systems. By developing a Neural -Fuzzy system learning with linguistic teaching signals. The advantage of this technique is that, produce a simple and well-performing system because it selects the fuzzy sets and the numerical numbers and process both numerical and linguistic information. This approach is able to process and learn numerical information as well as linguistic information. The proposed control scheme is applied to a multi-area power system with hydraulic and thermal turbines.
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.
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.
A Mixed Binary-Real NSGA II Algorithm Ensuring Both Accuracy and Interpretabi...IJECEIAES
In this work, a Neuro-Fuzzy Controller network, called NFC that implements a Mamdani fuzzy inference system is proposed. This network includes neurons able to perform fundamental fuzzy operations. Connections between neurons are weighted through binary and real weights. Then a mixed binaryreal Non dominated Sorting Genetic Algorithm II (NSGA II) is used to perform both accuracy and interpretability of the NFC by minimizing two objective functions; one objective relates to the number of rules, for compactness, while the second is the mean square error, for accuracy. In order to preserve interpretability of fuzzy rules during the optimization process, some constraints are imposed. The approach is tested on two control examples: a single input single output (SISO) system and a multivariable (MIMO) system.
This document describes a study that developed a neuro-fuzzy system for predicting electricity consumption. The neuro-fuzzy system combines the learning capabilities of neural networks with the linguistic rule interpretation of fuzzy inference systems. It was applied to predict future electricity consumption in Northern Cyprus based on past consumption data. The system was trained using a supervised learning algorithm to determine optimal parameters. Simulation results showed the neuro-fuzzy system achieved more accurate predictions of electricity consumption than a neural network model alone, using fewer training epochs.
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.
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.
An Artificial Intelligence Approach to Ultra High Frequency Path Loss Modelli...ijtsrd
This study proposes Artificial Intelligence AI based path loss prediction models for the suburban areas of Abuja, Nigeria. The AI based models were created on the bases of two deep learning networks, namely the Adaptive Neuro Fuzzy Inference System ANFIS and the Generalized Radial Basis Function Neural network RBF NN . These prediction models were created, trained, validated and tested for path loss prediction using path loss data recorded at 1800MHz from multiple Base Transceiver Stations BTSs distributed across the areas under investigation. Results indicate that the ANFIS and RBF NN based models with Root Mean Squared Error RMSE values of 5.30dB and 5.31dB respectively, offer greater prediction accuracy over the widely used empirical COST 231 Hata, which has an RMSE of 8.18dB. Deme C. Abraham ""An Artificial Intelligence Approach to Ultra-High Frequency Path Loss Modelling of the Suburban Areas of Abuja, Nigeria"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30227.pdf
Paper Url : https://www.ijtsrd.com/computer-science/artificial-intelligence/30227/an-artificial-intelligence-approach-to-ultra-high-frequency-path-loss-modelling-of-the-suburban-areas-of-abuja-nigeria/deme-c-abraham
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.
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.
A Mixed Binary-Real NSGA II Algorithm Ensuring Both Accuracy and Interpretabi...IJECEIAES
In this work, a Neuro-Fuzzy Controller network, called NFC that implements a Mamdani fuzzy inference system is proposed. This network includes neurons able to perform fundamental fuzzy operations. Connections between neurons are weighted through binary and real weights. Then a mixed binaryreal Non dominated Sorting Genetic Algorithm II (NSGA II) is used to perform both accuracy and interpretability of the NFC by minimizing two objective functions; one objective relates to the number of rules, for compactness, while the second is the mean square error, for accuracy. In order to preserve interpretability of fuzzy rules during the optimization process, some constraints are imposed. The approach is tested on two control examples: a single input single output (SISO) system and a multivariable (MIMO) system.
This document describes a study that developed a neuro-fuzzy system for predicting electricity consumption. The neuro-fuzzy system combines the learning capabilities of neural networks with the linguistic rule interpretation of fuzzy inference systems. It was applied to predict future electricity consumption in Northern Cyprus based on past consumption data. The system was trained using a supervised learning algorithm to determine optimal parameters. Simulation results showed the neuro-fuzzy system achieved more accurate predictions of electricity consumption than a neural network model alone, using fewer training epochs.
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.
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.
An Artificial Intelligence Approach to Ultra High Frequency Path Loss Modelli...ijtsrd
This study proposes Artificial Intelligence AI based path loss prediction models for the suburban areas of Abuja, Nigeria. The AI based models were created on the bases of two deep learning networks, namely the Adaptive Neuro Fuzzy Inference System ANFIS and the Generalized Radial Basis Function Neural network RBF NN . These prediction models were created, trained, validated and tested for path loss prediction using path loss data recorded at 1800MHz from multiple Base Transceiver Stations BTSs distributed across the areas under investigation. Results indicate that the ANFIS and RBF NN based models with Root Mean Squared Error RMSE values of 5.30dB and 5.31dB respectively, offer greater prediction accuracy over the widely used empirical COST 231 Hata, which has an RMSE of 8.18dB. Deme C. Abraham ""An Artificial Intelligence Approach to Ultra-High Frequency Path Loss Modelling of the Suburban Areas of Abuja, Nigeria"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30227.pdf
Paper Url : https://www.ijtsrd.com/computer-science/artificial-intelligence/30227/an-artificial-intelligence-approach-to-ultra-high-frequency-path-loss-modelling-of-the-suburban-areas-of-abuja-nigeria/deme-c-abraham
Investigations on Hybrid Learning in ANFISIJERA Editor
Neural networks have attractiveness to several researchers due to their great closeness to the structure of the brain, their characteristics not shared by many traditional systems. An Artificial Neural Network (ANN) is a network of interconnected artificial processing elements (called neurons) that co-operate with one another in order to solve specific issues. ANNs are inspired by the structure and functional aspects of biological nervous systems. Neural networks, which recognize patterns and adopt themselves to cope with changing environments. Fuzzy inference system incorporates human knowledge and performs inferencing and decision making. The integration of these two complementary approaches together with certain derivative free optimization techniques, results in a novel discipline called Neuro Fuzzy. In Neuro fuzzy development a specific approach is called Adaptive Neuro Fuzzy Inference System (ANFIS), which has shown significant results in modeling nonlinear functions. The basic idea behind the paper is to design a system that uses a fuzzy system to represent knowledge in an interpretable manner and have the learning ability derived from a Runge-Kutta learning method (RKLM) to adjust its membership functions and parameters in order to enhance the system performance. The problem of finding appropriate membership functions and fuzzy rules is often a tiring process of trial and error. It requires users to understand the data before training, which is usually difficult to achieve when the database is relatively large. To overcome these problems, a hybrid of Back Propagation Neural network (BPN) and RKLM can combine the advantages of two systems and avoid their disadvantages.
This document describes a study using artificial neural networks (ANNs) to model complex nonlinear systems. Specifically, it discusses:
1) Using an ANN to predict pressure distributions on a rotor wing during ramping motion, with results showing accurate prediction of spatial and temporal evolution.
2) Applying the same ANN model to predict performance of a bank stock based on trends in the stock and stock market index.
3) Proposing a framework combining ANNs with mathematical models to obtain better predictions and representations of financial data trends.
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for
comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
Comparison of Neural Network Training Functions for Hematoma Classification i...IOSR Journals
Classification is one of the most important task in application areas of artificial neural networks
(ANN).Training neural networks is a complex task in the supervised learning field of research. The main
difficulty in adopting ANN is to find the most appropriate combination of learning, transfer and training
function for the classification task. We compared the performances of three types of training algorithms in feed
forward neural network for brain hematoma classification. In this work we have selected Gradient Descent
based backpropagation, Gradient Descent with momentum, Resilence backpropogation algorithms. Under
conjugate based algorithms, Scaled Conjugate back propagation, Conjugate Gradient backpropagation with
Polak-Riebreupdates(CGP) and Conjugate Gradient backpropagation with Fletcher-Reeves updates (CGF).The
last category is Quasi Newton based algorithm, under this BFGS, Levenberg-Marquardt algorithms are
selected. Proposed work compared training algorithm on the basis of mean square error, accuracy, rate of
convergence and correctness of the classification. Our conclusion about the training functions is based on the
simulation results
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 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
1. Self-organizing maps (SOM) are an unsupervised learning algorithm that transform high-dimensional data into lower dimensions for visualization while preserving topological properties.
2. The SOM network has an input layer fully connected to an output layer arranged in a grid, with each node containing a weight vector of the same dimension as inputs.
3. During training, the best matching unit (BMU) and its neighbors on the grid have their weight vectors adjusted to better match the input based on their distance from the BMU, with learning rates decreasing over time.
DCT AND DFT BASED BIOMETRIC RECOGNITION AND MULTIMODAL BIOMETRIC SECURITYIAEME Publication
This Research paper discusses the study and analysis conducted during this research on various techniques in biometric domain. A close glance on biometric enhancement techniques and their limitations are presented in this research paper. This process would enable researcher to understand the research contributions in the area of DCT and DFT based recognition and security, locate some crucial limitations of these notable research. This paper having summary about the different research papers that applicable to our topic of research which mentioned above. Biometric Recognition and security is a most important subject of research in this area of image processing.
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.
DESIGN AND IMPLEMENTATION OF BINARY NEURAL NETWORK LEARNING WITH FUZZY CLUSTE...cscpconf
In this paper, Design and Implementation of Binary Neural Network Learning with Fuzzy
Clustering (DIBNNFC), is proposed to classify semisupervised data, it is based on the
concept of binary neural network and geometrical expansion. Parameters are updated
according to the geometrical location of the training samples in the input space, and each
sample in the training set is learned only once. It’s a semisupervised based approach, the
training samples are semi-labelled i.e. for some samples, labels are known and for some
samples data labels are not known. The method starts with classification, which is done by
using the concept of ETL algorithm. In classification process various classes are formed.
These classes classify samples in to two classes after that considers each class as a region and calculates the average of the entire region separately. This average is centres of the region which is used for the purpose of clustering by using FCM algorithm. Once clustering process over labelling of semi supervised data is done, then whole samples would be classify by (DIBNNFC). The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. The result reported, using real character recognition data set and result will compare with existing semi-supervised classifier, the proposed approach learned with semi-supervised leads to higher classification accuracy.
INTERVAL TYPE-2 INTUITIONISTIC FUZZY LOGIC SYSTEM FOR TIME SERIES AND IDENTIF...ijfls
This paper proposes a sliding mode control-based learning of interval type-2 intuitionistic fuzzy logic system for time series and identification problems. Until now, derivative-based algorithms such as gradient descent back propagation, extended Kalman filter, decoupled extended Kalman filter and hybrid method of decoupled extended Kalman filter and gradient descent methods have been utilized for the optimization of the parameters of interval type-2 intuitionistic fuzzy logic systems. The proposed model is based on a Takagi-Sugeno-Kang inference system. The evaluations of the model are conducted using both real world and artificially generated datasets. Analysis of results reveals that the proposed interval type-2 intuitionistic fuzzy logic system trained with sliding mode control learning algorithm (derivative-free) do outperforms some existing models in terms of the test root mean squared error while competing favourable with other models in the literature. Moreover, the proposed model may stand as a good choice for real time applications where running time is paramount compared to the derivative-based models.
This paper presents a novel approach called LOcal Rule Extraction (LORE) to extract rules from neural networks. LORE transforms a trained multilayer perceptron network into an equivalent decision diagram form to extract logic rules that generalize the network's output for inputs similar to the training set, while relaxing this condition for other inputs. It works by deriving a partial rule for each training sample, merging these rules, and then generalizing the merged rule set over the entire input space. The extracted rules are assessed based on their accuracy, fidelity to the original network, consistency, comprehensibility, and the computational complexity of the extraction process.
Intelligent Controller Design for a Chemical ProcessCSCJournals
Abstract - Chemical process control is a challenging problem due to the strong on-line non-linearity and extreme sensitivity to disturbances of the process. Ziegler – Nichols tuned PI and PID controllers are found to provide poor performances for higher-order and non–linear systems. This paper presents an application of one-step-ahead fuzzy as well as ANFIS (adaptive-network-based fuzzy inference system) tuning scheme for an Continuous Stirred Tank Reactor CSTR process. The controller is designed based on a Mamdani type and Sugeno type fuzzy system constructed to model the dynamics of the process. The fuzzy system model can take advantage of both a priori linguistic human knowledge through parameter initialization, and process measurements through on- line parameter adjustment. The ANFIS, which is a fuzzy inference system, is implemented in the framework of adaptive networks. The proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In this method, a novel approach based on tuning of fuzzy logic control as well as ANFIS for a CSTR process, capable of providing an optimal performance over the entire operating range of process are given. Here Fuzzy logic control as well as ANFIS for obtaining the optimal design of the CSTR process is explained. In this approach, the development of rule based and the formation of the membership function are evolved simultaneously. The performance of the algorithm in obtaining the optimal tuning values has been analyzed in CSTR process through computer simulation.
BACKPROPAGATION LEARNING ALGORITHM BASED ON LEVENBERG MARQUARDT ALGORITHMcscpconf
Data Mining aims at discovering knowledge out of data and presenting it in a form that is easily
compressible to humans. Data Mining represents a process developed to examine large amounts
of data routinely collected. The term also refers to a collection of tools used to perform the
process. One of the useful applications in the field of medicine is the incurable chronic disease
diabetes. Data Mining algorithm is used for testing the accuracy in predicting diabetic status.
Fuzzy Systems are been used for solving a wide range of problems in different application
domain and Genetic Algorithm for designing. Fuzzy systems allows in introducing the learning
and adaptation capabilities. Neural Networks are efficiently used for learning membership
functions. Diabetes occurs throughout the world, but Type 2 is more common in the most
developed countries. The greater increase in prevalence is however expected in Asia and Africa
where most patients will likely be found by 2030. This paper is proposed on the Levenberg –
Marquardt algorithm which is specifically designed to minimize sum-of-square error functions.
Levernberg-Marquardt algorithm gives the best performance in the prediction of diabetes compared to any other backpropogation algorithm
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 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.
Soft computing is a set of computational techniques that aim to mimic human-like reasoning and decision making. The main techniques are fuzzy logic, neural networks, evolutionary computing, machine learning, and probabilistic reasoning. Each technique has strengths and weaknesses, but they complement each other. When used together, soft computing techniques can solve complex problems that are difficult for traditional mathematical methods. The paper reviews these soft computing techniques and explores how they could be applied to problems in various domains.
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...IAEME Publication
Artificial neural networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between the elements. Neural networks with feedback connections provide a computing model capable of exploiting fine- grained parallelism to solve a rich class of complex problems. In this paper we discuss a complex series-parallel system subjected to finite common cause and finite human error failures and its reliability using neural network method.
This document discusses using repeated simulations of a crisp neural network to obtain quasi-fuzzy weight sets (QFWS) that can be used to initialize fuzzy neural networks. The key points are:
1) A crisp neural network is repeatedly trained on input-output data to model an unknown function. The connection weights change with each simulation.
2) Recording the weights from multiple simulations produces quasi-fuzzy weight sets, where each weight is a fuzzy set rather than a single value.
3) These QFWS can provide initial solutions for training type-I fuzzy neural networks with reduced computational complexity compared to random initialization.
4) The QFWS follow fuzzy arithmetic and allow both numerical and linguistic data to
This document provides an overview of applications of fuzzy logic in neural networks. It discusses fuzzy neurons as a combination of fuzzy logic and neural networks where the neuron's activation function is replaced with a fuzzy logic operation. Different types of fuzzy neurons are described, including OR, AND, and OR/AND fuzzy neurons. Supervised learning in fuzzy neural networks is also covered. The document concludes with advantages of fuzzy logic systems over traditional neural networks, such as the ability of fuzzy systems to systematically include linguistic knowledge.
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.
Investigations on Hybrid Learning in ANFISIJERA Editor
Neural networks have attractiveness to several researchers due to their great closeness to the structure of the brain, their characteristics not shared by many traditional systems. An Artificial Neural Network (ANN) is a network of interconnected artificial processing elements (called neurons) that co-operate with one another in order to solve specific issues. ANNs are inspired by the structure and functional aspects of biological nervous systems. Neural networks, which recognize patterns and adopt themselves to cope with changing environments. Fuzzy inference system incorporates human knowledge and performs inferencing and decision making. The integration of these two complementary approaches together with certain derivative free optimization techniques, results in a novel discipline called Neuro Fuzzy. In Neuro fuzzy development a specific approach is called Adaptive Neuro Fuzzy Inference System (ANFIS), which has shown significant results in modeling nonlinear functions. The basic idea behind the paper is to design a system that uses a fuzzy system to represent knowledge in an interpretable manner and have the learning ability derived from a Runge-Kutta learning method (RKLM) to adjust its membership functions and parameters in order to enhance the system performance. The problem of finding appropriate membership functions and fuzzy rules is often a tiring process of trial and error. It requires users to understand the data before training, which is usually difficult to achieve when the database is relatively large. To overcome these problems, a hybrid of Back Propagation Neural network (BPN) and RKLM can combine the advantages of two systems and avoid their disadvantages.
This document describes a study using artificial neural networks (ANNs) to model complex nonlinear systems. Specifically, it discusses:
1) Using an ANN to predict pressure distributions on a rotor wing during ramping motion, with results showing accurate prediction of spatial and temporal evolution.
2) Applying the same ANN model to predict performance of a bank stock based on trends in the stock and stock market index.
3) Proposing a framework combining ANNs with mathematical models to obtain better predictions and representations of financial data trends.
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for
comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
Comparison of Neural Network Training Functions for Hematoma Classification i...IOSR Journals
Classification is one of the most important task in application areas of artificial neural networks
(ANN).Training neural networks is a complex task in the supervised learning field of research. The main
difficulty in adopting ANN is to find the most appropriate combination of learning, transfer and training
function for the classification task. We compared the performances of three types of training algorithms in feed
forward neural network for brain hematoma classification. In this work we have selected Gradient Descent
based backpropagation, Gradient Descent with momentum, Resilence backpropogation algorithms. Under
conjugate based algorithms, Scaled Conjugate back propagation, Conjugate Gradient backpropagation with
Polak-Riebreupdates(CGP) and Conjugate Gradient backpropagation with Fletcher-Reeves updates (CGF).The
last category is Quasi Newton based algorithm, under this BFGS, Levenberg-Marquardt algorithms are
selected. Proposed work compared training algorithm on the basis of mean square error, accuracy, rate of
convergence and correctness of the classification. Our conclusion about the training functions is based on the
simulation results
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 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
1. Self-organizing maps (SOM) are an unsupervised learning algorithm that transform high-dimensional data into lower dimensions for visualization while preserving topological properties.
2. The SOM network has an input layer fully connected to an output layer arranged in a grid, with each node containing a weight vector of the same dimension as inputs.
3. During training, the best matching unit (BMU) and its neighbors on the grid have their weight vectors adjusted to better match the input based on their distance from the BMU, with learning rates decreasing over time.
DCT AND DFT BASED BIOMETRIC RECOGNITION AND MULTIMODAL BIOMETRIC SECURITYIAEME Publication
This Research paper discusses the study and analysis conducted during this research on various techniques in biometric domain. A close glance on biometric enhancement techniques and their limitations are presented in this research paper. This process would enable researcher to understand the research contributions in the area of DCT and DFT based recognition and security, locate some crucial limitations of these notable research. This paper having summary about the different research papers that applicable to our topic of research which mentioned above. Biometric Recognition and security is a most important subject of research in this area of image processing.
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.
DESIGN AND IMPLEMENTATION OF BINARY NEURAL NETWORK LEARNING WITH FUZZY CLUSTE...cscpconf
In this paper, Design and Implementation of Binary Neural Network Learning with Fuzzy
Clustering (DIBNNFC), is proposed to classify semisupervised data, it is based on the
concept of binary neural network and geometrical expansion. Parameters are updated
according to the geometrical location of the training samples in the input space, and each
sample in the training set is learned only once. It’s a semisupervised based approach, the
training samples are semi-labelled i.e. for some samples, labels are known and for some
samples data labels are not known. The method starts with classification, which is done by
using the concept of ETL algorithm. In classification process various classes are formed.
These classes classify samples in to two classes after that considers each class as a region and calculates the average of the entire region separately. This average is centres of the region which is used for the purpose of clustering by using FCM algorithm. Once clustering process over labelling of semi supervised data is done, then whole samples would be classify by (DIBNNFC). The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. The result reported, using real character recognition data set and result will compare with existing semi-supervised classifier, the proposed approach learned with semi-supervised leads to higher classification accuracy.
INTERVAL TYPE-2 INTUITIONISTIC FUZZY LOGIC SYSTEM FOR TIME SERIES AND IDENTIF...ijfls
This paper proposes a sliding mode control-based learning of interval type-2 intuitionistic fuzzy logic system for time series and identification problems. Until now, derivative-based algorithms such as gradient descent back propagation, extended Kalman filter, decoupled extended Kalman filter and hybrid method of decoupled extended Kalman filter and gradient descent methods have been utilized for the optimization of the parameters of interval type-2 intuitionistic fuzzy logic systems. The proposed model is based on a Takagi-Sugeno-Kang inference system. The evaluations of the model are conducted using both real world and artificially generated datasets. Analysis of results reveals that the proposed interval type-2 intuitionistic fuzzy logic system trained with sliding mode control learning algorithm (derivative-free) do outperforms some existing models in terms of the test root mean squared error while competing favourable with other models in the literature. Moreover, the proposed model may stand as a good choice for real time applications where running time is paramount compared to the derivative-based models.
This paper presents a novel approach called LOcal Rule Extraction (LORE) to extract rules from neural networks. LORE transforms a trained multilayer perceptron network into an equivalent decision diagram form to extract logic rules that generalize the network's output for inputs similar to the training set, while relaxing this condition for other inputs. It works by deriving a partial rule for each training sample, merging these rules, and then generalizing the merged rule set over the entire input space. The extracted rules are assessed based on their accuracy, fidelity to the original network, consistency, comprehensibility, and the computational complexity of the extraction process.
Intelligent Controller Design for a Chemical ProcessCSCJournals
Abstract - Chemical process control is a challenging problem due to the strong on-line non-linearity and extreme sensitivity to disturbances of the process. Ziegler – Nichols tuned PI and PID controllers are found to provide poor performances for higher-order and non–linear systems. This paper presents an application of one-step-ahead fuzzy as well as ANFIS (adaptive-network-based fuzzy inference system) tuning scheme for an Continuous Stirred Tank Reactor CSTR process. The controller is designed based on a Mamdani type and Sugeno type fuzzy system constructed to model the dynamics of the process. The fuzzy system model can take advantage of both a priori linguistic human knowledge through parameter initialization, and process measurements through on- line parameter adjustment. The ANFIS, which is a fuzzy inference system, is implemented in the framework of adaptive networks. The proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In this method, a novel approach based on tuning of fuzzy logic control as well as ANFIS for a CSTR process, capable of providing an optimal performance over the entire operating range of process are given. Here Fuzzy logic control as well as ANFIS for obtaining the optimal design of the CSTR process is explained. In this approach, the development of rule based and the formation of the membership function are evolved simultaneously. The performance of the algorithm in obtaining the optimal tuning values has been analyzed in CSTR process through computer simulation.
BACKPROPAGATION LEARNING ALGORITHM BASED ON LEVENBERG MARQUARDT ALGORITHMcscpconf
Data Mining aims at discovering knowledge out of data and presenting it in a form that is easily
compressible to humans. Data Mining represents a process developed to examine large amounts
of data routinely collected. The term also refers to a collection of tools used to perform the
process. One of the useful applications in the field of medicine is the incurable chronic disease
diabetes. Data Mining algorithm is used for testing the accuracy in predicting diabetic status.
Fuzzy Systems are been used for solving a wide range of problems in different application
domain and Genetic Algorithm for designing. Fuzzy systems allows in introducing the learning
and adaptation capabilities. Neural Networks are efficiently used for learning membership
functions. Diabetes occurs throughout the world, but Type 2 is more common in the most
developed countries. The greater increase in prevalence is however expected in Asia and Africa
where most patients will likely be found by 2030. This paper is proposed on the Levenberg –
Marquardt algorithm which is specifically designed to minimize sum-of-square error functions.
Levernberg-Marquardt algorithm gives the best performance in the prediction of diabetes compared to any other backpropogation algorithm
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 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.
Soft computing is a set of computational techniques that aim to mimic human-like reasoning and decision making. The main techniques are fuzzy logic, neural networks, evolutionary computing, machine learning, and probabilistic reasoning. Each technique has strengths and weaknesses, but they complement each other. When used together, soft computing techniques can solve complex problems that are difficult for traditional mathematical methods. The paper reviews these soft computing techniques and explores how they could be applied to problems in various domains.
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...IAEME Publication
Artificial neural networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between the elements. Neural networks with feedback connections provide a computing model capable of exploiting fine- grained parallelism to solve a rich class of complex problems. In this paper we discuss a complex series-parallel system subjected to finite common cause and finite human error failures and its reliability using neural network method.
This document discusses using repeated simulations of a crisp neural network to obtain quasi-fuzzy weight sets (QFWS) that can be used to initialize fuzzy neural networks. The key points are:
1) A crisp neural network is repeatedly trained on input-output data to model an unknown function. The connection weights change with each simulation.
2) Recording the weights from multiple simulations produces quasi-fuzzy weight sets, where each weight is a fuzzy set rather than a single value.
3) These QFWS can provide initial solutions for training type-I fuzzy neural networks with reduced computational complexity compared to random initialization.
4) The QFWS follow fuzzy arithmetic and allow both numerical and linguistic data to
This document provides an overview of applications of fuzzy logic in neural networks. It discusses fuzzy neurons as a combination of fuzzy logic and neural networks where the neuron's activation function is replaced with a fuzzy logic operation. Different types of fuzzy neurons are described, including OR, AND, and OR/AND fuzzy neurons. Supervised learning in fuzzy neural networks is also covered. The document concludes with advantages of fuzzy logic systems over traditional neural networks, such as the ability of fuzzy systems to systematically include linguistic knowledge.
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.
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.
Analysis of intelligent system design by neuro adaptive controliaemedu
This document summarizes the analysis of intelligent system design using neuro-adaptive control methods. It discusses using neural networks for system identification through series-parallel and parallel models. It also discusses supervised control using a neural network trained by an expert operator, inverse control using a neural network trained on the inverse system model, and neuro-adaptive control using two neural networks - one for system identification and one for control. Neuro-adaptive control allows handling nonlinear system behavior without linear approximations.
New artificial neural network design for Chua chaotic system prediction usin...IJECEIAES
This study aims to design a new architecture of the artificial neural networks (ANNs) using the Xilinx system generator (XSG) and its hardware co-simulation equivalent model using field programmable gate array (FPGA) to predict the behavior of Chua’s chaotic system and use it in hiding information. The work proposed consists of two main sections. In the first section, MATLAB R2016a was used to build a 3×4×3 feed forward neural network (FFNN). The training results demonstrate that FFNN training in the Bayesian regulation algorithm is sufficiently accurate to directly implement. The second section demonstrates the hardware implementation of the network with the XSG on the Xilinx artix7 xc7a100t-1csg324 chip. Finally, the message was first encrypted using a dynamic Chua system and then decrypted using ANN’s chaotic dynamics. ANN models were developed to implement hardware in the FPGA system using the IEEE 754 Single precision floating-point format. The ANN design method illustrated can be extended to other chaotic systems in general.
This document summarizes a study that compares fuzzy logic and neuro-fuzzy models for predicting direct current in motors. Fuzzy logic and neuro-fuzzy systems were used to model the relationship between motor torque, power, speed (inputs) and current (output). Both techniques were tested on a dataset of 507 samples. The neuro-fuzzy inference system (ANFIS) performed slightly better than the fuzzy logic system at predicting motor current, demonstrating the benefits of combining fuzzy logic with neural networks.
The document discusses using clustering models like subtractive fuzzy clustering (SFC) and fuzzy c-means clustering (FCM) to generate an adaptive neuro-fuzzy inference system (ANFIS) for medical diagnoses. Experimental results on medical diagnosis datasets show that ANFIS models using SFC and FCM clustering (ANFIS-SFC and ANFIS-FCM) had better average training and checking errors compared to ANFIS without clustering. Specifically, ANFIS-SFC performed best using backpropagation learning, while ANFIS-FCM performed best using a hybrid learning model. Clustering the datasets without ANFIS was also able to identify different disease clusters.
A Threshold Logic Unit (TLU) is a mathematical function conceived as a crude model, or abstraction of biological neurons. Threshold logic units are the constitutive units in an artificial neural network. In this paper a positive clock-edge triggered T flip-flop is designed using Perceptron Learning Algorithm, which is a basic design algorithm of threshold logic units. Then this T flip-flop is used to design a two-bit up-counter that goes through the states 0, 1, 2, 3, 0, 1… Ultimately, the goal is to show how to design simple logic units based on threshold logic based perceptron concepts.
This document proposes a new method for extracting rules from trained multilayer artificial neural networks that can represent rules in both "if-then" and "M of N" formats. The method extracts an intermediate structure called a "generator list" from which both types of rules can be derived. This provides a more generic representation than existing methods that can only output one rule format. The generator list approach avoids preprocessing steps used in other methods that can modify the original network. It uses heuristics to prune the search space when extracting the generator list to address the computational complexity involved.
A Novel Neuroglial Architecture for Modelling Singular Perturbation System IJECEIAES
This document summarizes a research paper that proposes a novel artificial neuroglial network (ANGN) architecture for modeling singular perturbation systems. The ANGN is inspired by the human brain where information flows along fast neural and slow glial pathways. The ANGN uses modular design and algorithms based on multi-timescale systems. It was tested on an asynchronous machine model in singularly perturbed standard form. The ANGN achieved smaller, simpler networks with strong nonlinear approximation abilities, outperforming conventional neural networks for modeling nonlinear singular perturbation systems.
Artificial Neural Networks (ANNS) For Prediction of California Bearing Ratio ...IJMER
The behaviour of soil at the location of the project and interactions of the earth materials during and after construction has a major influence on the success, economy and safety of the work. Another complexity associated with some geotechnical engineering materials, such as sand and gravel, is the difficulty in obtaining undisturbed samples and time consuming involving skilled
technician. Knowledge of California Bearing Ratio (C.B.R) is essential in finding the road thickness. To cope up with the difficulties involved, an attempt has been made to model C.B.R in terms of Fine Fraction, Liquid Limit, Plasticity Index, Maximum Dry density, and Optimum Moisture content. A multi-layer perceptron network with feed forward back propagation is used to model varying the
number of hidden layers. For this purposes 50 soils test data was collected from the laboratory test
results. Among the test data 30 soils data is used for training and remaining 20 soils for testing using
60-40 distribution. The architectures developed are 5-4-1, 5-5-1, and 5-6-1. Model with 5-6-1 architecture is found to be quite satisfactory in predicting C.B.R of soils. A graph is plotted between
the predicted values and observed values of outputs for training and testing process, from the graph it
is found that all the points are close to equality line, indicating predicted values are close to observed
values
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.
X-TREPAN: A MULTI CLASS REGRESSION AND ADAPTED EXTRACTION OF COMPREHENSIBLE D...cscpconf
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
The document describes an algorithm called X-TREPAN that extracts decision trees from trained neural networks. X-TREPAN is an enhancement of the TREPAN algorithm that allows it to handle both multi-class classification and multi-class regression problems. It can also analyze generalized feed forward networks. The algorithm was tested on several real-world datasets and was found to generate decision trees with good classification accuracy while also maintaining comprehensibility.
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.
NETWORK LEARNING AND TRAINING OF A CASCADED LINK-BASED FEED FORWARD NEURAL NE...ijaia
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.
Modeling of neural image compression using gradient decent technologytheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
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This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
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This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Chapter wise All Notes of First year Basic Civil Engineering.pptx
A Learning Linguistic Teaching Control for a Multi-Area Electric Power System
1. Ahmad N. Al-Husban
International Journal of Engineering (IJE), Volume (6) : Issue (5) : 2012 278
A Learning Linguistic Teaching Control for a Multi-Area
Electric Power System
Ahmad N. AL- Husban drahusban2008@yahoo.com
Faculty of Engineering Technology
AL-Balqa Applied University
Amman, 11947, Jordan
Abstract
This paper presents a new methodology for designing a neuro-fuzzy control for complex physical
systems. By developing a Neural -Fuzzy system learning with linguistic teaching signals. The
advantage of this technique is that, produce a simple and well-performing system because it
selects the fuzzy sets and the numerical numbers and process both numerical and linguistic
information. This approach is able to process and learn numerical information as well as
linguistic information. The proposed control scheme is applied to a multi-area power system with
hydraulic and thermal turbines.
Keywords: Fuzzy Logic Control, Artificial Neural Network, Interconnected Power System, Load
Frequency Control, Neuro-fuzzy Systems.
1. INTRODUCTION
The control engineer’s knowledge of the system is based on expertise, intuition, knowledge of the
system’s behavior. Therefore, the main objective of the fuzzy control scheme is to replace an
expert human operator with a fuzzy rule-based control system.
The fuzzy system belongs to a general class of fuzzy logic system in which fuzzy system
variables are transformed into fuzzy sets “Fuzzification” and manipulated by a collection of “IF-
THEN” fuzzy rules, assembled in what is known as the fuzzy inference engine.
These rules are derived from the knowledge of experts with substantial experience in the system.
Then, the fuzzy sets are transformed into fuzzy variables” Defuzzification” [2, 4].
In such a system, input values are normalized and converted to fuzzy representations, the
model’s rule base is executed to produce a consequent fuzzy region for each solution variable,
and the consequent regions are defuzzified to find the expected value of each solution variable
[1, 7].
Artificial Neural networks may be employed to represent the brain activities, neural networks are
attractive to the classical techniques for identification and control of complex physical systems,
because of their ability to learn and approximate functions [6, 9].
The conventional control systems usually involve the development of a mathematical model of
the system to derive a control law. In many of the physical systems, it may be difficult to obtain
an accurate mathematical model due to the presence of structured and unstructured
uncertainties. Fuzzy system and neural networks are both soft computing approaches for
modeling expert behavior [7, 9]. This paper will show those combinations of neural networks with
fuzzy systems, the so called neural fuzzy or neuro-fuzzy systems.
By a Neuro-fuzzy system, one understands a system which involves in some way both fuzzy
systems and neural networks, or features of both, combined in a single system.
2. Ahmad N. Al-Husban
International Journal of Engineering (IJE), Volume (6) : Issue (5) : 2012 279
The most important reason for combining fuzzy systems with neural networks is their learning
capability and such a combination should be able to learn linguistic rules and / or membership
functions.
Therefore, combining neural networks with a fuzzy set could combine the advantage of symbolic
and numerical processing.
Neural Networks and fuzzy systems estimate functions from sample data, it does not require a
mathematical model; they are model-free estimators [6, 9]
2. FUZZY LOGIC CONTROLLER
The fuzzy logic controller comprises three stages namely fuzzifier, rule-based assignment tables
and the defuzzifier. The fuzzifier is responsible for converting crisp measured data into suitable
linguistic values. The fuzzy rule-base stores the empirical knowledge of the operation of the
domain experts. The inference engine is the kernel of an FLC, and it has the capability of
simulating human decision-making by performing approximation reasoning to achieve a desired
output.
The defuzzifier is utilized to yield a nonfuzzy decision action from an inferred fuzzy system by the
inference engine. The defuzzifier is responsible for converting linguistic values into crisp data [1,
7].
A typical architecture of a fuzzy logic is shown in Fig.1
FIGURE 1: Fuzzy System Structure
The fuzzy logic system proceeds as follows to evaluate the desired output signal, as shown in
Fig.2.
At First, the input variables are normalized, and the membership function of the fuzzy logic
controller output signal is determined by linguistic codes
Finally, the numerical value of the adaptive fuzzy logic controller output signal corresponding to a
specific linguistic code is determined.
FIGURE 2: The Internal Structure of Fuzzy Logic
The error “e” and the error change “∆e” are defined as a difference between the set point value
and the current output value
Norm 1
Norm 2Delay
Fuzzy
Logic
O/p
Fuzzy Rule-Base
Fuzzifier Defuzzifier
Fuzzy Inference Engine
X in U
Y in V
Fuzzy Set in U Fuzzy Set in V
3. Ahmad N. Al-Husban
International Journal of Engineering (IJE), Volume (6) : Issue (5) : 2012 280
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kekeke
kukuke cr
(1)
(2)
That is,
)1()( 00
−= kuku rr
This assumption is also satisfied in most cases:
Case (1):
)1()(
)()(
0)(0)(
0
−<
<∆⇒
>∆<
kukuand
kuku
keandke
m
c
m
c
m
cr
Case (2):
)1()(
)()(
0)(0)(
0
−<
>∆⇒
<∆>
kukuand
kuku
keandke
m
c
m
c
m
cr
Where
ur
m
(k): is the reference of the fuzzy logic controller at k-th sampling interval
uc
m
(k): is the fuzzy logic controller signal at k-th sampling interval
e(k) is the error signal
∆e(k): is the error change signal
3. NEURAL NETWORKS
The most significant characteristic of the neural networks is their ability to approximate arbitrary
nonlinear functions. This ability of the neural networks has made them useful to model nonlinear
systems, which is of primary importance in the synthesis of nonlinear controllers [11]. A neuro-
controller (neural network-based control system), in general, performs a specific form of a
multilayer network and the adaptive parameters being defined as the adjustable weights [12].
In general, neural networks represent parallel-distributed processing structures, which make them
prime candidates for use in multivariable control systems.
The neural network approach defines the problem of control as the mapping of measured signals
for change into calculated controls for actions. The system shown in Fig.3 represents the neural
learning and control scheme, a control system is called a learning control system, if the
information pertaining to the unknown features of the system for its environment is acquired
during operation, and the obtained information is used for future estimation.
4. Ahmad N. Al-Husban
International Journal of Engineering (IJE), Volume (6) : Issue (5) : 2012 281
FIGURE 3: A Typical Neural Learning and Control Scheme
4. NEURO-FUZZY SYSTEMS
The neural networks and fuzzy systems solve problems by performing the function
approximation. Neural networks can be used, if training data is available, and a mathematical
model of the system is not needed [6-9, 11, 12]. But a fuzzy system can be used, if knowledge
about the solution of the problem in the form of linguistic IF-THEN rules is available, a formal
model of the system is unnecessary, and training data will not be needed.
On the other hand, if the problem of interest changes too much compared to the former training
data, then the network may not to able to cope with that, there is no guarantee that resuming the
training process will lead to fast adaption to the modified problem, it may be necessary to repeat
the learning again [11].
A neuro-fuzzy system, is a system which involves in some way both fuzzy systems and neural
networks or features of both combined in a single system [11, 12]
FIGURE 4: The five Layer Architecture
Fig. 4 shows the neural fuzzy system structure with five-layers. The proposed approach to
develop a neuro-fuzzy logic control consists of the following five steps. At first, each node in the
first layer transmits input number xi to the next layer directly.
The second step is called matching, each node in the 2
nd
layer has exactly one input from some
input linguistic nodes and feeds its output to rule node, and the weight is fuzzy number w. the
third step.
The input and output of a node in the 3
rd
layer are numerically calculated to find the minimum
matching of fuzzy logic rules. The fourth step, finds the maximum value for the 3
rd
layer, and the
nodes in the 4
th
layer should be fuzzy OR. Finally, Merging and Defuzzification of each node has
a fuzzy weight w Yi.
All previous steps can be governed by the following equations
5. Ahmad N. Al-Husban
International Journal of Engineering (IJE), Volume (6) : Issue (5) : 2012 282
iiii XOOUO =−= )( 1
2
1
1
1
(3)
2
1
2
1
2
))((
))((5.0
tuXw
tuXwf
ijij
ijijij
∑
∑
−×
+−×=
(4)
),........,min( 33
1
3
1 kuuO = (5)
),........,max( 44
1
3
1 kuuO = (6)
∑
∑=== 5
1
5
15
2
5
15 ),(
u
wyu
YOOUO (7)
5. ELECTRIC POWER SYSTEM
It is reasonable to study in considerable details the megawatt frequency control problem for multi-
area electric power system. Load Frequency Control “LFC” is a very important factor in power
system operation. It aims at controlling the output power of each generator to minimize the
transient errors in the frequency and tie-line power deviations and to ensure its zero steady state
errors [15, 16].
Load frequency control “LFC” sometimes, called Automatic Generation Control “AGC” is a very
important aspect in power system operation and control for supplying sufficient and reliable
electric power with the desired quality [13, 14].
Load frequency control generally involves several designed power areas within an integrated
power grid with each area responsible for controlling its area control error ‘ACE”
A two area interconnected power system model is developed. The load frequency control “LFC”
of interconnected power system “IPS” relies on an operating schedule that is usually prepared all
day. In advance, this schedule indicates the expected demand profile of the area as well as, the
area commitment to its adjacent areas. The problem of the LFC of an IPS can be expressed
mathematically as follows
],,,,,,,[
],,,,,,,[
22222111
87654321
tielggggg PPHXFPXF
XXXXXXXXX
∆∆∆∆∆∆∆∆=
=
(8)
],[],,,[ 22114321 dcdc PPPPUUUUU ∆∆∆∆== (9)
The commitment is the tie-line power interchange which should be maintained at a certain point in
time. This value is fed to the other area. The functional block diagram of Hydro-Thermal
interconnected power system is shown in Fig.5. Power deficits may be purely active, purely
reactive, or combined. Any of these deficits affects the frequency of the system either directly.
6. Ahmad N. Al-Husban
International Journal of Engineering (IJE), Volume (6) : Issue (5) : 2012 283
FIGURE 5: Block diagram of Hydro-Thermal Interconnected power system
Due to active power unbalance, or indirectly, through change in system demand due to changes
in voltage caused by reactive power unbalance.
Active power deficits may take place in power systems as a result of forced outage of generating
units and / or loaded tie lines, or due to the switching on of appreciable loads.
The interlinking of the various areas in case of a two-area system is though the tie-line power
exchange. Changes in tie-line power flows affected the power balance in corresponding areas as
shown in Fig.6.
FIGURE 6: Tie -Line Power Exchange
The incremental tie-line power is
)sin()cos()sin(
)cos()sin(
0
2
0
1
0
2
0
1
0
12
0
2
0
1
0
12
0
2
0
1
0
2
0
1
0
122
δδδδδδ
δδδδ
−−+−−
−−=∆
TT
TPtiel
(10)
6. NUMERICAL DATA
As a numerical example, a two area load frequency control system was studied. The numerical
data has shown:
Thermal-area
M=0.04 , G=0.01 , Tg=0.5 , Tt=0.5, E=0.03
Hydro-area
M=0.03 , G=0.08 , Tg=0.5 , Tt=0.5, E=0.013, Tw=0.5
Tie-line power
T12=0.02701
T12
0
sin(.) Cos(.)
T12
0
cos(.) Sin(.)
Ptie∆ δ12δ12
E1
1
1+STg1
1
1+STt1
1
MS1+D1
E1
1
1+STg1
1
1+STt
1
1
MS1+D1
1-Sw
1+.5ST
∆Pc1
F1∆
∆Pc2
F2∆
Pd1∆
∆Pd2
7. Ahmad N. Al-Husban
International Journal of Engineering (IJE), Volume (6) : Issue (5) : 2012 284
In this section, the development of a Neuro-fuzzy system, learning with linguistic teaching signals
is shown.
This system is able to process and learn numerical information as well as linguistic information.
It can be used as an adaptive fuzzy controller by using the reinforcement learning proposed in
[12]. The proposed Neuro-fuzzy techniques confirmed the effectiveness of the human operator in
the presence of system nonlinearities. The results are shown in Fig.7, indicates the closed loop
response of the Neuro-fuzzy control.
0 2 4 6 8 10 12 14 16 18 20
-0.25
-0.2
-0.15
-0.1
-0.05
0
FREQUENCY DEVIATION
Time in Sec.
Thermal
Hydro
0 2 4 6 8 10 12 14 16 18 20
-8
-6
-4
-2
0
x 10
-3 TIE-LINE DEVIATION
Time in Sec.
FIGURE 7: the System Response with Neuro-Fuzzy Control
6. CONCLUSION
This paper proposed combining a neural network with a fuzzy set; It could combine the
advantage of symbolic and numerical processing. Neural Networks and fuzzy systems estimate
functions from sample data, it does not require a mathematical model; they are model-free
estimators a Neural-Fuzzy system that process both numerical and linguistic information. The
proposed system has some characteristics and advantages, the inputs and outputs are fuzzy
numbers or numerical numbers, the weights of the proposed Neural-fuzzy system are fuzzy
weights, owing to the representation forms of the fuzzy weights, the fuzzy inputs and fuzzy
outputs can be fuzzy number of any shape, and except the input-output layers, numerical
numbers are propagated through the whole Neural-fuzzy system. The proposed Neuro-fuzzy
techniques confirmed the effectiveness of the human operator in the presence of system
nonlinearities. This controller does not require the system model, this model is a complex model,
and needed to leanirezed to design a controller, but our controller require only the observation of
input-output. The response of Hydro-Thermal plant has an error less than the conventional
controller used, and it is an adaptive controller.
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8. Ahmad N. Al-Husban
International Journal of Engineering (IJE), Volume (6) : Issue (5) : 2012 285
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rd
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