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.
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.
Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) have shown popularity, superiority, and more accuracy in performance in a number of applications in the last decade. This is due to its ability to cope with uncertainty and precisions adequately when compared with its type-1 counterpart. In this paper, an Interval Type-2 Fuzzy Logic System (IT2FLS) is employed for remote vital signs monitoring and predicting of shock level in cardiac patients. Also, the conventional, Type-1 Fuzzy Logic System (T1FLS) is applied to the prediction problems for comparison purpose. The cardiac patients’ health datasets were used to perform empirical comparison on the developed system. The result of study indicated that IT2FLS could coped with more information and handled more uncertainties in health data than T1FLS. The statistical evaluation using performance metrices indicated a minimal error with IT2FLS compared to its counterpart, T1FLS. It was generally observed that the shock level prediction experiment for cardiac patients showed the superiority of IT2FLS paradigm over T1FLS.
LATTICE-CELL : HYBRID APPROACH FOR TEXT CATEGORIZATIONcsandit
In this paper, we propose a new text categorization framework based on Concepts Lattice and
cellular automata. In this framework, concept structure are modeled by a Cellular Automaton
for Symbolic Induction (CASI). Our objective is to reduce time categorization caused by the
Concept Lattice. We examine, by experiments the performance of the proposed approach and
compare it with other algorithms such as Naive Bayes and k nearest neighbors. The results
show performance improvement while reducing time categorization.
The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...Waqas Tariq
Selection of inputs is one of the most substantial components of classification algorithms for data mining and pattern recognition problems since even the best classifier will perform badly if the inputs are not selected very well. Big data and computational complexity are main cause of bad performance and low accuracy for classical classifiers. In other words, the complexity of classifier method is inversely proportional with its classification efficiency. For this purpose, two hybrid classifiers have been developed by using both type-1 and type-2 fuzzy c-means clustering with cascaded a classifier. In this proposed classifier, a large number of data points are reduced by using fuzzy c-means clustering before applied to a classifier algorithm as inputs. The aim of this study is to investigate the effect of fuzzy clustering on well-known and useful classifiers such as artificial neural networks (ANN) and support vector machines (SVM). Then the role of positive effects of these proposed algorithms were investigated on applied different data sets.
Fuzzy modeling require two main steps which are structure identification and parameter optimization, the
first one determines the numbers of membership functions and fuzzy if-then rules, while the second
identifies a feasible set of parameters under the given structure. However, the increase of input dimension,
rule numbers will have an exponential growth and there will cause problem of “rule disaster”. In this
paper, we have applied adaptive network fuzzy inference system ANFIS for phonemes recognition. The
appropriate learning algorithm is performed on TIMIT speech database supervised type, a pre-processing
of the acoustic signal and extracting the coefficients MFCCs parameters relevant to the recognition system.
First learning of the network structure by subtractive clustering, in order to define an optimal structure and
obtain small number of rules, then learning of parameters network by hybrid learning which combine the
gradient decent and least square estimation LSE to find a feasible set of antecedents and consequents
parameters. The results obtained show the effectiveness of the method in terms of recognition rate and
number of fuzzy rules generated.
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.
Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) have shown popularity, superiority, and more accuracy in performance in a number of applications in the last decade. This is due to its ability to cope with uncertainty and precisions adequately when compared with its type-1 counterpart. In this paper, an Interval Type-2 Fuzzy Logic System (IT2FLS) is employed for remote vital signs monitoring and predicting of shock level in cardiac patients. Also, the conventional, Type-1 Fuzzy Logic System (T1FLS) is applied to the prediction problems for comparison purpose. The cardiac patients’ health datasets were used to perform empirical comparison on the developed system. The result of study indicated that IT2FLS could coped with more information and handled more uncertainties in health data than T1FLS. The statistical evaluation using performance metrices indicated a minimal error with IT2FLS compared to its counterpart, T1FLS. It was generally observed that the shock level prediction experiment for cardiac patients showed the superiority of IT2FLS paradigm over T1FLS.
LATTICE-CELL : HYBRID APPROACH FOR TEXT CATEGORIZATIONcsandit
In this paper, we propose a new text categorization framework based on Concepts Lattice and
cellular automata. In this framework, concept structure are modeled by a Cellular Automaton
for Symbolic Induction (CASI). Our objective is to reduce time categorization caused by the
Concept Lattice. We examine, by experiments the performance of the proposed approach and
compare it with other algorithms such as Naive Bayes and k nearest neighbors. The results
show performance improvement while reducing time categorization.
The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...Waqas Tariq
Selection of inputs is one of the most substantial components of classification algorithms for data mining and pattern recognition problems since even the best classifier will perform badly if the inputs are not selected very well. Big data and computational complexity are main cause of bad performance and low accuracy for classical classifiers. In other words, the complexity of classifier method is inversely proportional with its classification efficiency. For this purpose, two hybrid classifiers have been developed by using both type-1 and type-2 fuzzy c-means clustering with cascaded a classifier. In this proposed classifier, a large number of data points are reduced by using fuzzy c-means clustering before applied to a classifier algorithm as inputs. The aim of this study is to investigate the effect of fuzzy clustering on well-known and useful classifiers such as artificial neural networks (ANN) and support vector machines (SVM). Then the role of positive effects of these proposed algorithms were investigated on applied different data sets.
Fuzzy modeling require two main steps which are structure identification and parameter optimization, the
first one determines the numbers of membership functions and fuzzy if-then rules, while the second
identifies a feasible set of parameters under the given structure. However, the increase of input dimension,
rule numbers will have an exponential growth and there will cause problem of “rule disaster”. In this
paper, we have applied adaptive network fuzzy inference system ANFIS for phonemes recognition. The
appropriate learning algorithm is performed on TIMIT speech database supervised type, a pre-processing
of the acoustic signal and extracting the coefficients MFCCs parameters relevant to the recognition system.
First learning of the network structure by subtractive clustering, in order to define an optimal structure and
obtain small number of rules, then learning of parameters network by hybrid learning which combine the
gradient decent and least square estimation LSE to find a feasible set of antecedents and consequents
parameters. The results obtained show the effectiveness of the method in terms of recognition rate and
number of fuzzy rules generated.
Sparse representation based classification of mr images of brain for alzheime...eSAT Publishing House
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
Mining association rules is one of the most important data mining tasks. Its purpose is to
generate intelligible relations between attributes in a database. However, its use in practice is
difficult and still raises several challenges, in particular, the number of learned rules is often
very large. Several techniques for reducing the number of rules have been proposed as
measures of quality, syntactic filtering constraints, etc. However, these techniques do not limit
the shortcomings of these methods. In this paper, we propose a new approach to mine
association, assisted by a Boolean modeling of results in order to mitigate the shortcomings
mentioned above and propose a cellular automaton based on a boolean process for mining,
optimizing, managing and representing of the learned rules.
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.
FUZZY CONTROL OF A SERVOMECHANISM: PRACTICAL APPROACH USING MAMDANI AND TAKAG...ijfls
The main objective of this work is to propose two fuzzy controllers: one based on the Mamdani inference
method and another controller based on the Takagi- Sugeno inference method, both will be designed for
application in a position control system of a servomechanism. Some comparations between the methods
mentioned above will be made with regard to the performance of the system in order to identify the
advantages of the Takagi- Sugeno method in relation to the Mamdani method in the presence of
disturbances and nonlinearities of the system. Some results of simulation and practical application are
presented and results obtained showed that controllers based on Takagi- Sugeno method is more efficient
than controllers based on Mamdani method for this specific application.
Telecardiology and Teletreatment System Design for Heart Failures Using Type-...Waqas Tariq
Proper diagnosis of heart failures is critical, since the appropriate treatments are strongly dependent upon the underlying cause. Furthermore, rapid diagnosis is also critical, since the effectiveness of some treatments depends upon rapid initiation. In this paper, a new web-based telecardiology system has been proposed for diagnosis, consultation, and treatment. The aim of this implemented telecardiology system is to help to practitioner doctor, if clinic findings of patient misgive heart failures. This model consists of three subsystems. The first subsystem divides into recording and preprocessing phase. Here, electrocardiography signal is recorded from emergency patient and this recorded signal is preprocessed for detection of RR interval. The second subsystem realizes classification of RR interval. In other words, this second subsystem is to diagnosis heart failures. In this study, a combined classification system has been designed using type-2 fuzzy c-means clustering (T2FCM) algorithm and neural networks. T2FCM was used to improve performance of neural networks which was obtained very high performance accuracy to classify RR intervals of ECG signals. This proposed automated telecardiology and diagnostic system assists to practitioner doctor to diagnosis heart failures easily. Training and testing data for this diagnostic system are included five ECG signal classes. The third subsystem is consultation and teletreatment between practitioner (or family) doctor and cardiologist worked in research hospital with prepared web page (www.telekardiyoloji.com). However, opportunity of signal’s evaluation is presented to practitioner and expert doctor with prepared interfaces. T2FCM is applied to the training data for the selection of best segments in the second subsystem. A new training set formed by these best segments was classified using the neural networks classifier which has backpropagation well-known algorithm and generalized delta rule learning. Recognition accuracy rate was found as 99% using proposed Type-2 Fuzzy Clustering Neural Networks (T2FCNN) method.
PREDICTIVE EVALUATION OF THE STOCK PORTFOLIO PERFORMANCE USING FUZZY CMEANS A...ijfls
The aim of this paper is to investigate the trend of the return of a portfolio formed randomly or for any
specific technique. The approach is made using two techniques fuzzy: fuzzy c-means (FCM) algorithm and
the fuzzy transform, where the rules used at fuzzy transform arise from the application of the FCM
algorithm. The results show that the proposed methodology is able to predict the trend of the return of a
stock portfolio, as well as the tendency of the market index. Real data of the financial market are used from
2004 until 2007.
The process of determining cuts tuition for students are usually given with the same nominal. And in this paper is the determination of the pieces tuition for students who are less able to be different, depending on how much income parents and the number of children covered. For income parents who get discounted tuition fee of IDR Rp.1,500,000 and for the number of children in these families also determine the number of pieces obtained. Tsukamoto Fuzzy system is the model used in this paper. Each input variable is divided into three membership functions. In this paper, Nine Tsukamoto Fuzzy model rules have been applied. The system also provides a consequent change of parameters if the current parameter values to be changed. The smaller the parent's income, the greater the pieces obtained. The more children insured the greater the college acquired pieces.
Filtering of Frequency Components for Privacy Preserving Facial RecognitionArtur Filipowicz
This paper examines the use of signal processing and feature engineering techniques to design a facial recognition system with image-reconstruction privacy protection. The Fast Fourier Transform (FFT) and Wavelet Transform (WT) are used to derive features from face images in the Yale and Olivetti datasets. Then, the features are selected by a filter. We propose several filters that fall into three categories – conventional filters (rectangular and triangular), unsupervised-learning filter (variance), and supervised-learning filter (SNR, FDR, SD, and t-test). Furthermore, we investigate the role of FFT phase removal as a possible tool for image reconstruction privacy protection. The results show that both filtering and FFT phase removal can prevent privacy-compromising reconstruction of the original images without sacrificing recognition accuracy. Among the filters, we found the SNR and t-test filters to yield the best recognition accuracies while preserving the image-reconstruction privacy. This work presents a great promise for signal processing and feature engineering as a tool toward building privacy-preserving facial recognition systems.
SPECIFICATION OF THE STATE’S LIFETIME IN THE DEVS FORMALISM BY FUZZY CONTROLLERijait
This paper aims to develop a new approach to assess the duration of state in the DEVS formalism by fuzzy
controller. The idea is to define a set of fuzzy rules obtained from observers or expert knowledge and to
specify a fuzzy model which computes this duration, this latter is fed into the simulator to specify the new
value in the model. In conventional model, each state is defined by a mean lifetime value whereas our
method, calculates for each state the new lifetime according to inputs values. A wildfire case study is
presented at the end of the paper. It is a challenging task due to its complex behavior, dynamical weather
condition, and various variables involved. A global specification of the fuzzy controller and the forest fire
model are presented in the DEVS formalism and comparison between conventional and fuzzy method is
illustrated.
ANALYTICAL FORMULATIONS FOR THE LEVEL BASED WEIGHTED AVERAGE VALUE OF DISCRET...ijsc
In fuzzy decision-making processes based on linguistic information, operations on discrete fuzzy numbers
are commonly performed. Aggregation and defuzzification operations are some of these often used
operations. Many aggregation and defuzzification operators produce results independent to the decisionmaker’s
strategy. On the other hand, the Weighted Average Based on Levels (WABL) approach can take
into account the level weights and the decision maker's "optimism" strategy. This gives flexibility to the
WABL operator and, through machine learning, can be trained in the direction of the decision maker's
strategy, producing more satisfactory results for the decision maker. However, in order to determine the
WABL value, it is necessary to calculate some integrals. In this study, the concept of WABL for discrete
trapezoidal fuzzy numbers is investigated, and analytical formulas have been proven to facilitate the
calculation of WABL value for these fuzzy numbers. Trapezoidal and their special form, triangular fuzzy
numbers, are the most commonly used fuzzy number types in fuzzy modeling, so in this study, such numbers
have been studied. Computational examples explaining the theoretical results have been performed.
A Learning Linguistic Teaching Control for a Multi-Area Electric Power SystemCSCJournals
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.
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.
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...ijcsa
The work is about using Simulated Annealing Algorithm for the effort estimation model parameter
optimization which can lead to the reduction in the difference in actual and estimated effort used in model
development.
The model has been tested using OOP’s dataset, obtained from NASA for research purpose.The data set
based model equation parameters have been found that consists of two independent variables, viz. Lines of
Code (LOC) along with one more attribute as a dependent variable related to software development effort
(DE). The results have been compared with the earlier work done by the author on Artificial Neural
Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) and it has been observed that the
developed SA based model is more capable to provide better estimation of software development effort than
ANN and ANFIS
Dynamic Evolving Neuro-Fuzzy Inference System for Mortality Prediction IJERA Editor
In this paper we propose a dynamic evolving neuro-fuzzy inference system (DENFIS) to forecast mortality. DENFIS is an adaptive intelligent system suitable for dynamic time series prediction. An Evolving Cluster Method (ECM) drives the learning process. The typical fuzzy rules of the neuro- fuzzy systems are updated during the learning process and adjusted according to the features of the data. This makes possible to capture the changes in the mortality evolution at the basis of the so called longevity risk
Sparse representation based classification of mr images of brain for alzheime...eSAT Publishing House
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
Mining association rules is one of the most important data mining tasks. Its purpose is to
generate intelligible relations between attributes in a database. However, its use in practice is
difficult and still raises several challenges, in particular, the number of learned rules is often
very large. Several techniques for reducing the number of rules have been proposed as
measures of quality, syntactic filtering constraints, etc. However, these techniques do not limit
the shortcomings of these methods. In this paper, we propose a new approach to mine
association, assisted by a Boolean modeling of results in order to mitigate the shortcomings
mentioned above and propose a cellular automaton based on a boolean process for mining,
optimizing, managing and representing of the learned rules.
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.
FUZZY CONTROL OF A SERVOMECHANISM: PRACTICAL APPROACH USING MAMDANI AND TAKAG...ijfls
The main objective of this work is to propose two fuzzy controllers: one based on the Mamdani inference
method and another controller based on the Takagi- Sugeno inference method, both will be designed for
application in a position control system of a servomechanism. Some comparations between the methods
mentioned above will be made with regard to the performance of the system in order to identify the
advantages of the Takagi- Sugeno method in relation to the Mamdani method in the presence of
disturbances and nonlinearities of the system. Some results of simulation and practical application are
presented and results obtained showed that controllers based on Takagi- Sugeno method is more efficient
than controllers based on Mamdani method for this specific application.
Telecardiology and Teletreatment System Design for Heart Failures Using Type-...Waqas Tariq
Proper diagnosis of heart failures is critical, since the appropriate treatments are strongly dependent upon the underlying cause. Furthermore, rapid diagnosis is also critical, since the effectiveness of some treatments depends upon rapid initiation. In this paper, a new web-based telecardiology system has been proposed for diagnosis, consultation, and treatment. The aim of this implemented telecardiology system is to help to practitioner doctor, if clinic findings of patient misgive heart failures. This model consists of three subsystems. The first subsystem divides into recording and preprocessing phase. Here, electrocardiography signal is recorded from emergency patient and this recorded signal is preprocessed for detection of RR interval. The second subsystem realizes classification of RR interval. In other words, this second subsystem is to diagnosis heart failures. In this study, a combined classification system has been designed using type-2 fuzzy c-means clustering (T2FCM) algorithm and neural networks. T2FCM was used to improve performance of neural networks which was obtained very high performance accuracy to classify RR intervals of ECG signals. This proposed automated telecardiology and diagnostic system assists to practitioner doctor to diagnosis heart failures easily. Training and testing data for this diagnostic system are included five ECG signal classes. The third subsystem is consultation and teletreatment between practitioner (or family) doctor and cardiologist worked in research hospital with prepared web page (www.telekardiyoloji.com). However, opportunity of signal’s evaluation is presented to practitioner and expert doctor with prepared interfaces. T2FCM is applied to the training data for the selection of best segments in the second subsystem. A new training set formed by these best segments was classified using the neural networks classifier which has backpropagation well-known algorithm and generalized delta rule learning. Recognition accuracy rate was found as 99% using proposed Type-2 Fuzzy Clustering Neural Networks (T2FCNN) method.
PREDICTIVE EVALUATION OF THE STOCK PORTFOLIO PERFORMANCE USING FUZZY CMEANS A...ijfls
The aim of this paper is to investigate the trend of the return of a portfolio formed randomly or for any
specific technique. The approach is made using two techniques fuzzy: fuzzy c-means (FCM) algorithm and
the fuzzy transform, where the rules used at fuzzy transform arise from the application of the FCM
algorithm. The results show that the proposed methodology is able to predict the trend of the return of a
stock portfolio, as well as the tendency of the market index. Real data of the financial market are used from
2004 until 2007.
The process of determining cuts tuition for students are usually given with the same nominal. And in this paper is the determination of the pieces tuition for students who are less able to be different, depending on how much income parents and the number of children covered. For income parents who get discounted tuition fee of IDR Rp.1,500,000 and for the number of children in these families also determine the number of pieces obtained. Tsukamoto Fuzzy system is the model used in this paper. Each input variable is divided into three membership functions. In this paper, Nine Tsukamoto Fuzzy model rules have been applied. The system also provides a consequent change of parameters if the current parameter values to be changed. The smaller the parent's income, the greater the pieces obtained. The more children insured the greater the college acquired pieces.
Filtering of Frequency Components for Privacy Preserving Facial RecognitionArtur Filipowicz
This paper examines the use of signal processing and feature engineering techniques to design a facial recognition system with image-reconstruction privacy protection. The Fast Fourier Transform (FFT) and Wavelet Transform (WT) are used to derive features from face images in the Yale and Olivetti datasets. Then, the features are selected by a filter. We propose several filters that fall into three categories – conventional filters (rectangular and triangular), unsupervised-learning filter (variance), and supervised-learning filter (SNR, FDR, SD, and t-test). Furthermore, we investigate the role of FFT phase removal as a possible tool for image reconstruction privacy protection. The results show that both filtering and FFT phase removal can prevent privacy-compromising reconstruction of the original images without sacrificing recognition accuracy. Among the filters, we found the SNR and t-test filters to yield the best recognition accuracies while preserving the image-reconstruction privacy. This work presents a great promise for signal processing and feature engineering as a tool toward building privacy-preserving facial recognition systems.
SPECIFICATION OF THE STATE’S LIFETIME IN THE DEVS FORMALISM BY FUZZY CONTROLLERijait
This paper aims to develop a new approach to assess the duration of state in the DEVS formalism by fuzzy
controller. The idea is to define a set of fuzzy rules obtained from observers or expert knowledge and to
specify a fuzzy model which computes this duration, this latter is fed into the simulator to specify the new
value in the model. In conventional model, each state is defined by a mean lifetime value whereas our
method, calculates for each state the new lifetime according to inputs values. A wildfire case study is
presented at the end of the paper. It is a challenging task due to its complex behavior, dynamical weather
condition, and various variables involved. A global specification of the fuzzy controller and the forest fire
model are presented in the DEVS formalism and comparison between conventional and fuzzy method is
illustrated.
ANALYTICAL FORMULATIONS FOR THE LEVEL BASED WEIGHTED AVERAGE VALUE OF DISCRET...ijsc
In fuzzy decision-making processes based on linguistic information, operations on discrete fuzzy numbers
are commonly performed. Aggregation and defuzzification operations are some of these often used
operations. Many aggregation and defuzzification operators produce results independent to the decisionmaker’s
strategy. On the other hand, the Weighted Average Based on Levels (WABL) approach can take
into account the level weights and the decision maker's "optimism" strategy. This gives flexibility to the
WABL operator and, through machine learning, can be trained in the direction of the decision maker's
strategy, producing more satisfactory results for the decision maker. However, in order to determine the
WABL value, it is necessary to calculate some integrals. In this study, the concept of WABL for discrete
trapezoidal fuzzy numbers is investigated, and analytical formulas have been proven to facilitate the
calculation of WABL value for these fuzzy numbers. Trapezoidal and their special form, triangular fuzzy
numbers, are the most commonly used fuzzy number types in fuzzy modeling, so in this study, such numbers
have been studied. Computational examples explaining the theoretical results have been performed.
A Learning Linguistic Teaching Control for a Multi-Area Electric Power SystemCSCJournals
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.
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.
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...ijcsa
The work is about using Simulated Annealing Algorithm for the effort estimation model parameter
optimization which can lead to the reduction in the difference in actual and estimated effort used in model
development.
The model has been tested using OOP’s dataset, obtained from NASA for research purpose.The data set
based model equation parameters have been found that consists of two independent variables, viz. Lines of
Code (LOC) along with one more attribute as a dependent variable related to software development effort
(DE). The results have been compared with the earlier work done by the author on Artificial Neural
Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) and it has been observed that the
developed SA based model is more capable to provide better estimation of software development effort than
ANN and ANFIS
Dynamic Evolving Neuro-Fuzzy Inference System for Mortality Prediction IJERA Editor
In this paper we propose a dynamic evolving neuro-fuzzy inference system (DENFIS) to forecast mortality. DENFIS is an adaptive intelligent system suitable for dynamic time series prediction. An Evolving Cluster Method (ECM) drives the learning process. The typical fuzzy rules of the neuro- fuzzy systems are updated during the learning process and adjusted according to the features of the data. This makes possible to capture the changes in the mortality evolution at the basis of the so called longevity risk
Fuzzy Control of a Servomechanism: Practical Approach using Mamdani and Takag...ijfls
The main objective of this work is to propose two fuzzy controllers: one based on the Mamdani inference method and another controller based on the Takagi- Sugeno inference method, both will be designed for application in a position control system of a servomechanism. Some comparations between the methods mentioned above will be made with regard to the performance of the system in order to identify the advantages of the Takagi- Sugeno method in relation to the Mamdani method in the presence of disturbances and nonlinearities of the system. Some results of simulation and practical application are presented and results obtained showed that controllers based on Takagi- Sugeno method is more efficient than controllers based on Mamdani method for this specific application.
MULTI-PARAMETER BASED PERFORMANCE EVALUATION OF CLASSIFICATION ALGORITHMSijcsit
Diabetes disease is amongst the most common disease in India. It affects patient’s health and also leads to
other chronic diseases. Prediction of diabetes plays a significant role in saving of life and cost. Predicting
diabetes in human body is a challenging task because it depends on several factors. Few studies have reported the performance of classification algorithms in terms of accuracy. Results in these studies are difficult and complex to understand by medical practitioner and also lack in terms of visual aids as they arepresented in pure text format. This reported survey uses ROC and PRC graphical measures toimproveunderstanding of results. A detailed parameter wise discussion of comparison is also presented which lacksin other reported surveys. Execution time, Accuracy, TP Rate, FP Rate, Precision, Recall, F Measureparameters are used for comparative analysis and Confusion Matrix is prepared for quick review of each
algorithm. Ten fold cross validation method is used for estimation of prediction model. Different sets of
classification algorithms are analyzed on diabetes dataset acquired from UCI repository
Although fuzzy systems demonstrate their ability to
solve different kinds of problems in various applications, there is an increasing interest on developing solid mathematical implementations suitable for control applications such as that used in fuzzy logic controllers (FLC). It is well known that, wide range of parameters is needed to be specified before the construction of a fuzzy system. To simplify in a systematic way the design and construction of a general fuzzy system, and without loss for generality a full parameterization process for a singleton type FLC is proposed in this paper. The resented methodology is very helpful in developing a universal computing algorithm for a standard fuzzy like PID controllers. An illustrative example shows the simplicity of applying the new paradigm.
An Algorithm of Policy Gradient Reinforcement Learning with a Fuzzy Controlle...Waqas Tariq
Typical fuzzy reinforcement learning algorithms take value-function based approaches, such as fuzzy Q-learning in Markov Decision Processes (MDPs), and use constant or linear functions in the consequent parts of fuzzy rules. Instead of taking such approaches, we propose a fuzzy reinforcement learning algorithm in another approach. That is the policy gradient approach. Our method can handle fuzzy sets even in the consequent part and also learn the rule weights of fuzzy rules. Specifically, we derived learning rules of membership functions and rule weights for both cases when input/output variables to/from the control system are discrete and continuous.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
In order to treat and analyze real datasets, fuzzy association rules have been proposed. Several
algorithms have been introduced to extract these rules. However, these algorithms suffer from
the problems of utility, redundancy and large number of extracted fuzzy association rules. The
expert will then be confronted with this huge amount of fuzzy association rules. The task of
validation becomes fastidious. In order to solve these problems, we propose a new validation
method. Our method is based on three steps. (i) We extract a generic base of non redundant
fuzzy association rules by applying EFAR-PN algorithm based on fuzzy formal concept analysis.
(ii) we categorize extracted rules into groups and (iii) we evaluate the relevance of these rules
using structural equation model.
ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM FOR SPEECH RECOGNITION THROUGH ...ijaia
Fuzzy modeling require two main steps which are structure identification and parameter optimization, the
first one determines the numbers of membership functions and fuzzy if-then rules, while the second
identifies a feasible set of parameters under the given structure. However, the increase of input dimension,
rule numbers will have an exponential growth and there will cause problem of “rule disaster”. In this
paper, we have applied adaptive network fuzzy inference system ANFIS for phonemes recognition. The
appropriate learning algorithm is performed on TIMIT speech database supervised type, a pre-processing
of the acoustic signal and extracting the coefficients MFCCs parameters relevant to the recognition system.
First learning of the network structure by subtractive clustering, in order to define an optimal structure and
obtain small number of rules, then learning of parameters network by hybrid learning which combine the
gradient decent and least square estimation LSE to find a feasible set of antecedents and consequents
parameters. The results obtained show the effectiveness of the method in terms of recognition rate and
number of fuzzy rules generated.
LATTICE-CELL : HYBRID APPROACH FOR TEXT CATEGORIZATIONcscpconf
In this paper, we propose a new text categorization framework based on Concepts Lattice and cellular automata. In this framework, concept structure are modeled by a Cellular Automaton for Symbolic Induction (CASI). Our objective is to reduce time categorization caused by the Concept Lattice. We examine, by experiments the performance of the proposed approach and compare it with other algorithms such as Naive Bayes and k nearest neighbors. The results show performance improvement while reducing time categorization.
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.
Improvement in Traditional Set Partitioning in Hierarchical Trees (SPIHT) Alg...AM Publications
In this paper, an improved SPIHT algorithm based on Huffman coding and discrete wavelet transform for
image compression has been developed. The traditional SPIHT algorithm application is limited in terms of PSNR and
Compression Ratio. The improved SPIHT algorithm gives better performance as compared with traditional SPIHT
algorithm; here we used additional Huffman encoder along with SPIHT encoder. Huffman coding is an entropy
encoding algorithm uses a specific method for choosing the representation for each symbol, resulting in a prefix code.
The input gray scale image is decomposed by 'bior4.4' wavelet and wavelet coefficients are indexed and scanned by
DWT, then applied to encode the coefficient by SPIHT encoder followed by Huffman encoder which gives the
compressed image. At receiver side the decoding process is applied by Huffman decoder followed by SPIHT decoder.
The Reconstructed gray scale image looks similar to the applied gray scale image.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...kevig
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...ijnlc
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is
also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
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
AN OPTIMAL FUZZY LOGIC SYSTEM FOR A NONLINEAR DYNAMIC SYSTEM USING A FUZZY BA...IJCNCJournal
The impetus for this paper is the development of Fuzzy Basis Function “FBF” that assigns in an optimal fashion, a function approximation for a nonlinear dynamic system. A fuzzy basis function is applied to find the best location of the characteristic points by specifying the set of fuzzy rules. The advantage of this technique is that, it may produce a simple and well-performing system because it selects the most significant fuzzy basis functions to minimize an objective function in the output error for the fuzzy rules. The fuzzy basis function is a linguistic fuzzy IF_THEN rule. It provides a combination of the numerical information and the linguistic information in the form input-output pairs and in the form of fuzzy rules. The proposed control scheme is applied to a magnetic ball suspension system.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...
INTERVAL TYPE-2 INTUITIONISTIC FUZZY LOGIC SYSTEM FOR TIME SERIES AND IDENTIFICATION PROBLEMS - A COMPARATIVE STUDY
1. International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020
DOI: 10.5121/ijfls.2020.10101 1
INTERVAL TYPE-2 INTUITIONISTIC FUZZY LOGIC
SYSTEM FOR TIME SERIES AND IDENTIFICATION
PROBLEMS - A COMPARATIVE STUDY
Imo Eyoh1
, Jeremiah Eyoh2
and Roy Kalawsky2
1
Department of Computer Science, University of Uyo, Akwa Ibom State, Nigeria
2
School of Electrical, Electronics and Systems Engineering, AVRRC Research Group,
Loughborough University, UK
ABSTRACT
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.
KEYWORDS
Interval type-2 intuitionistic fuzzy set, Sliding mode control algorithm, Intuitionistic fuzzy set,
Intuitionistic fuzzy index.
1. INTRODUCTION
The classical FS including both the T1FS [1] and T2FS [2]are defined using the membership
functions. Literature has it that classical FSs are complementary sets [3]. This implies that to
compute the non-membership of a classical FS, one has to take the complement of the set so
defined. This may not always be the case in real life contexts because people are often times
hesitant to pin-point or specify a single numerical value as doing so indicate strong commitments
or evidence.
This brings the idea of intuitionistic FS (IFS) introduced by Atanassov in 1986 [4]. With IFS, a
set can be described by three components namely: membership function, non-membership
function and hesitation index. With these three representations, IFS becomes a more appropriate
tool for dealing with imprecise and vague information. While the non-membership function
captures additional information, the hesitation index makes the set description very intuitive and
close to human intelligence than classical FS.
2. International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020
2
However, using single membership and non-membership values to model a concept may not
capture enough information and uncertainty in the intuitionistic set definition. This is because
changes in the perception of the same linguistic term will lead to frequent re-tuning of the
membership and non-membership functions of IFSs. This may result to waste of resources and
sub-optimal performance of the system under some environmental and operation conditions.
Atanassov and Gargov [5] extended the concept of the IFS to interval valued intuitionistic fuzzy
set (IVIFS),with interval values similar to the notion of classical interval valued fuzzy set (IVFS)
[6]. To obtain an IVIFS, the sum of upper membership and upper non-membership must not
exceed 1. Based on the salient points discussed above for IFS and IVIFS, Eyoh et al., [7]
introduced a rule-based IT2IFLS that merges the capabilities of IT2FLS with those of IFS. The
IT2IFLS relaxes the single restriction of the IVIFS by allowing two constraints namely that the
sum of upper membership and lower non-membership must not exceed 1, similarly the sum of
lower membership and upper non-membership must not exceed 1. This makes IT2IFS more
flexible than IVIFS.
RELATED WORK
Several methods such as gradient descent (GD), extended Kalman filter (EKF), decoupled EKF
(DEKF) and the hybrid (DEKF and GD) have been applied so far for the optimization of
IT2IFLS. The optimization may be structure or parameter optimization.In [7] IT2IFLS is
proposed and the parameters of the model are updated using GD back-propagation method (a first
order derivative-based learning algorithm). The proposed model is applied to non-linear system
prediction with good results. The same model is also applied to time series prediction [8].
Recently, Luo et al., [9], proposed an evolving recurrent interval type-2 intuitionistic fuzzy
neural network (IT2IFNN) and applied the model for online learning and time series
prediction. The parameters of the IT2IFNN in [9] are optimized using EKF (a second-order
derivative-based method). In [10], the DEKF is adopted for the optimization of the parameters of
the IT2IFLS. Using the DEKF allows the parameters of the model to be grouped into vectors such
as antecedent and consequent vectors so that interactions are allowed at the second order.
Experimental results reveal that EKF-based learning models perform better than the GD-based in
terms of prediction accuracy. In [11], a hybrid model of GD back-propagation and DEKF is
employed for the adjustment of the parameters of IT2IFLS and the model applied to system
identification problem.Recently also, Yuan and Luo [12] proposed an online evolving
interval type-2 intuitionistic fuzzy LSTM-Neural Networks (eIT2IF-LSTMNN). In [12]
and [13], the parameters of the models are optimized using GD-back-propagationand
applied for regression problems.Other studies on IT2IFS is the one reported in [14], where
arithmetic operations are defined for IT2IFS using generalized trapezoidal type-2 intuitionistic
fuzzy numbers. The proposed model is used to solve a transportation problem. In this work, only
the parameters of the IT2IFLS are optimized. According to [15], membership function (non-
membership function) parameters of FLSs are very important in deciding the overall performance
of the system.
The weakness of GD and EKF-based models is that the GD and EKF-based methods are both
derivative-based approaches and involve the computation of partial derivatives for both the
membership and non-membership functions which is tedious and time consuming. For large scale
computation, these derivative-based approaches may not be the best choice.Unlike the derivative-
based models, using SMC learning algorithm to update the IT2IFLS parameters is straightforward
3. International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020
3
and relieves the system of the computational burden of calculating the partial derivatives of these
parameters.
This paper proposes the optimization of the IT2IFLS parameters using derivative-free
methodology with application to time series and prediction problems. In this study, a sliding
mode control learning of the parameters of IT2IFLS is developed and used to solve time series
and identification problems for the first time. The antecedent and the consequent parameters
update benefits from the proposed learning algorithm.
The rest of the paper is structured as follows: In section 2, some underlying concepts are defined.
Section 3 takes a view of interval type-2 intuitionistic fuzzy logic system. In Section 4, sliding
mode control learning algorithm is defined with the update rule using sliding mode algorithm
derived in Section 5. Experimental evaluation is carried out in Section 6 with the conclusion
drawn in Section 7.
2. DEFINITIONS
DEFINITION 1: An IFS is totally defined by membership function, non-membership function and
hesitation index (π) of element, x ∊ X such that 0 ≤ A*( ) + νA*( ) + ( ) = 1[4].
When the intuitionistic index is 0, the IFS changed to classical FS. Many approaches have been
adopted in the literature for constructing membership and non-membership functions of IFS.
Some of the approaches include those reported in [16][17][18]. The IFS Gaussian membership
and non-membership function in definitions proposed in [17] are adopted in this paper and are
defined as follows:
μ(x) = (1 - ( )) * exp (− )2
(1)
ν(x) = (1 - ( )) - μ(x) (2)
DEFINITION 2: An IT2IFS, Ã* is a variant of IFS but membership and non-membership functions
are themselves fuzzy and defined as {μÃ* (x), μÃ* (x)} and {vÃ* (x), vÃ* (x)} respectively for all x
∈X with constraints: 0 ≤ μÃ*(x) + vÃ* (x) ≤ 1 and 0 ≤ μÃ* (x) + vÃ* (x) ≤ 1 [19]
For IT2IFS, two footprints of uncertainties (FOUs) are utilized which are membership function
FOU and non-membership function FOU (see Figure 1).
FOUμ (Ã*) = ⋃ [μÃ∗, μÃ∗]∀ ∈' (3)
FOUν (Ã*) = ⋃ [vÃ∗, vÃ∗]∀ ∈' (4)
As shown in Figure 1, for designing the non-membership function FOU, the lower membership
function becomes the upper non-membership function while the upper membership function
becomes the lower non-membership function [20].
4. International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020
4
Figure 1: An IT2 intuitionistic Gaussian membership and non-membership functions
FOU
3. INTERVAL TYPE-2 INTUITIONISTIC FUZZY LOGIC SYSTEM
An interval type-2 fuzzy logic system consist of intuitionistic - fuzzifier, rulebase, inference
engine and output processing block (type reduction and defuzzifier) as shown in Figure 2. The
intuitionistic fuzzifier converts crisp exogenous inputs into IT2IFS, with exactly four components
namely lower membership, upper-membership, lower non-membership and upper non-
membership. Here, singleton fuzzification is considered. The hesitation index ensures that the
sum of lower membership and upper non-membership functions in the input partition space is less
than or equal to 1 and similarly, the sum of upper membership and lower non-membership
functions is less than or equal to 1. In this study, the approach for constructing membership and
non-membership functions reported in [17] is adopted and modified to reflect the type-2 version
as follows:
μ./( .) = exp (−
012,34
)2
* (1- ( .)) (5)
μ./( .) = exp (− 05,34
)2
* (1- ( .)) (6)
v./( .) = (1- π1 ( .)) - μ./( .) (7)
v./( .) = (1-π ( .)) - μ./( .) (8)
where and π are the intuitionistic fuzzy indices for center and variance respectively [17].
The and π are small numbers in the range of 0 and 1. The intuitionistic fuzzy indices can be
chosen by the user or randomly generated. In this study, and π are randomly generated in
the interval [0,1].
The intuitionistic fuzzy indices use in this study are expressed for IT2IFLS as follows:
c( ) = max (0, (1- (µÃ* ( ) + νÃ* ( )))) (9)
5. International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020
5
var( ) = max (0, (1 – (μÃ* ( ) + vÃ* ( )))) (10)
var( ) = max (0, (1- (μÃ* ( ) + vÃ* ( )))) (11)
such that 0≤ c( ) ≤ 1 and 0 ≤ var ( ) ≤ 1
Figure 2: Interval type-2 intuitionistic fuzzy logic system [20]
The generic IT2IFLS IF-THEN rule is represented as follows:
7/ : IF . is Ã./
∗
and … and 8 is Ã8/
∗
THEN 9/ = ∑ ;./
8
.<= . + >/ (12)
The generic rule can be formulated for membership and non-membership functions respectively
as follows:
7/
?
: IF . is Ã∗
./
?
and … and 8 is Ã∗
8/
?
THEN 9/ = ∑ ;./
?8
.<= + >/
?
(13)
7/
@
: IF . is Ã∗
./
@
and … and 8 is Ã∗
8/
@
THEN 9/ = ∑ ;./
@8
.<= + >/
@
(14)
Where Ã./
∗
s, are IT2IFS, 9/are the ABC rule outputs for membership function and non-
membership function, ; and >are parameters of the consequent parts. The inference engine maps
IT2IFS inputs to IT2IFS outputs using the combinations of the formulated rules. According to
[7], the final output of a TSK-type IT2IFLS is defined as follows:
y = (1 - D) ∑ EF
/
?G
/<= 9/
?
+ (1 - D) ∑ EF/
@G
/<= 9/
@
(15)
where:
EF
/
?
=
(H4
I
J H̅
4
I
)
∑ H4
IL
4M5 J∑ H̅
4
IL
4M5
(16)
EF/
@
=
(H4
N
J H̅4
N
)
∑ H4
NL
4M5 J∑ H̅
4
NL
4M5
(17)
6. International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020
6
and EF
/
?
and EF/
@
are normalized firing signals for membership and non-membership functions
respectively.
4. SLIDING MODE CONTROL LEARNING
The error of IT2IFLS is defined as follows:
E(t) = 9 (t) – 9(t) (18)
The error shows the difference between the measured output and the predicted output of the
model. The zero value of the error coordinate is shown as time varying sliding surface [21] as
below:
S(E(t)) = E(t) = 9 (t) – 9(t) = 0 (19)
With this,the system which is in a sliding mode is guaranteed to be on a sliding. In this way, the
IT2IFLS output will match the actual output as close as possible for all time t >OC, where OC is the
hitting time for E(t) = 0.
DEFINITION 3: A sliding motion will be on a sliding manifold S(E(t)) = E(t) = 0 after a time OC if
the condition S(t)PQ (t) < 0 is valid ∀O in some nontrivial semi open subinterval of time of the form
[t, OC) ⊂ (-∞, OC) [22][23]
5. PARAMETER UPDATE
In this study, the SMC learning is used to update the parameters of IT2IFLS. The antecedent
parameters are center (c), lower membership function standard deviation (T) and upper
membership function standard deviation (T) while w and b are consequent parameters with D as
the user defined parameter. The update rules of the IT2IFLS parameters are adapted from [24] for
IT2FLS as follows for the antecedent parameters:
For the membership function parameter update, we have the following:
UQ./ = Q. + ( .-U./)V=sgn(E) (20)
TQ./ = - (T./ + 34
W
( 3 34)2)V=sgn(E) (21)
TQ./= - (T./ +
134
W
( 3 34)2)V=sgn(E) (22)
where V= is the learning rate and E is the learning error. Since the lower non-membership
assumes the upper membership function and vice versa, the standard deviation update for the
upper membership function applies to standard deviation update for the lower non-membership
function and vice versa.
8. International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020
8
231 data points are used for training while 100 data points are used for testing. The input-output
vector is [x(t-4), x(t-3), x(t-2), x(t-1); x(t+1)], where x(t+1) is the desired output. For the four
inputs NSW electricity dataset, 16 rules are generated with 192 parameters. The dataset for each
season is normalized to fall within the range of zero and one and the performance computed over
30 simulations with the root mean squared error (RMSE) and mean absolute error (MAE) as the
performance metrics. The RMSE and MAE are computed as below:
RMSE = `
=
a
∑ (9 − 9b)a
.<=
2
(26)
MAE =
=
a
∑ |9 − 9b
|a
.<= (27)
where N is the total number of test data, 9 and 9b
are the actual and predicted outputs
respectively.
Table 1: Performance of different models during Summer season.
Table 2: Performance of different models during Autumn season.
Table 3: Performance of different models during Winter season.
9. International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020
9
Table 4: Performance of different models during Spring season.
As indicated in tables 1 to 4, IT2IFLS-SMC outperforms other IT2IFLS models in terms of the
test RMSE during the Summer and Spring seasons and competes favourable with other models in
the other instances. The DEKF trained models tend to exhibit better prediction accuracy in terms
of training RMSE in most instances while the type-1 version do perform better in some cases, an
indication that a type-1 variant of FLS may work well in some instances depending on the type of
data and level of uncertainty in the data.There is a loss in the performance SMC trained IT2IFLS
in terms of the training RMSE. However, the computational cost of IT2IFLS-SMC is much more
impressive than other competing models in all the cases. For example, the running times for
IT2IFLS-SMC, GD and DEKF for one instance of NSW electricity data are 25.78secs, 28.03secs
and 44.75secs respectively. Hence IT2IFLS-SMC may stands as an appropriate choice in real
time applications where running time is paramount.However, using the SMC learning algorithm
with the sign function exhibitedsome chattering effects in the system during the evaluation of the
model. This might also explain the poor performance of IT2IFLS-SMC in the training RMSE.
6.2. Canadian Lynx Dataset
The Canadian lynx time series dataset consist of the number of lynx trapped in the McKenzie
river annually in Northern Canada and is taken from period 1821 to 1934. To aid comparison
with existing studies [26][27][28][10] the logarithms to base 10 of the Canadian time series is
adopted with a periodicity of 10 years. The Canadian time series has a total of 114 sample points
where 100 instances are used for training and the rest are used for testing. For a fair comparison
with previous studies, the experiment is run for 2000 epochs. The quality of prediction is
measured using the mean squared error (MSE) and MAE. The MSE is computed as below:
MSE =
=
a
∑ (9 − 9b
)a
.<=
2
(28)
While the MAE is as shown in (26).
10. International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020
10
: (a) (b)
Figure 3 (a) shows original Canadian lynx data and (b) transformed Canadian lynx data (log10)
Table 5: Comparison of IT2IFLS with other models on Canadian lynx data
11. International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020
11
Figure 4: Actual and predicted output of Canadian lynx data
Figure 4shows the actual and predicted values of Canadian lynx data. As shown in Table 5,
IT2IFLS-SMC performs better than other existing models in the literature. The SMC-based
IT2IFLS prediction accuracy is very close to the GD-based IT2IFLS in this problem domain.
6.3. System identification problem 1
Here, comparison of IT2IFLS-SMC learning with other existing methods in the literature using a
system identification problem is conducted. In the first non-linear dynamic system identification,
we adopt a second-order time-varying system defined as:y(t+1) = f(y(t), y(t-1), y(t-2), u(t), u(t-1))
where f( =, , d, e, f ) = 5, 2, W , g ( W h) J i
J 2
2 J W
2 (29)
and a, b, and c are time-varying parameters defined as below:
a(t) = 1.2 - 0.2cos(2 O/k) (30)
b(t) = 1.0 - 0.4sin(2 O/k) (31)
c(t) = 1.0 - 0.4sin(2 O/k) (32)
Here, T = 1000 represents the total number of sample points. The parameters a, b and c take the
value 1 as reported in [9]. Similar to [38], in order to keep a manageable number of parameters,
only two inputs values are utilised which are u(t) and y(t) with y(t +1) as the desired output. The
plot of u(t) versus y(t) is as shown in Figure 5.
12. International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020
12
Figure 5: Plot showing the relationship between the two input u and y for system
identification 1.
The following control signal is used to test the performance of the non-linear system.
l
m
n
m
o sin r
sB
f
t O < 250
1.0 250 ≤ O < 500
− 1.0 500 ≤ O < 750
0.3 sinr
sB
f
t + 0.1 sinr
sB
d
t + 0.6 sin r
sB
=|
t 750 ≤ O < 1000
} (33)
The simulation is conducted for 1000 time steps with 100 training epochs. The performance
metrics used is the RMSE. For the non-linear system identification, 4 rules are obtained with 40
tunable parameters.
Table 6: Performance comparison of IT2IFLS with other models on system identification 1
13. International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020
13
Figure 6: Actual and predicted outputs for system identification problem 1
Shown in Table 6 is the performance comparison of IT2IFLS-SMC with other learning models in
the literature. As shown in Table 6, the performance of IT2IFLS is comparative to those reported
in the literature especially the evolving classical IT2FLS methods reported in [9]. Figure 6 shows
the actual and predicted outcome for system identification problem 1.
6.4. System identification problem 2
For the second identification problem, a non-linear dynamic plant with longer input delay is
adopted and defined as:
y(t+1)=0.72y(t) + 0.025y(t-1)u(t-1) + 0.01u2
(t-2)+0.2u(t-3) (34)
Equation (34) involves a one-step ahead prediction using two previous outputs and four previous
inputs. The test signal in (34) is adopted to test the quality of prediction. Similar to [12], we adopt
the same training data and time steps as in system identification 1. To reduce the complexity of
the system, two inputs u(t) andy(t)are passed into the IT2IFLS. Shown in Figure 6 is the
relationship between the two inputs u and y for system identification 2.
Figure 7: Plot showing the relationship between the u and y in system identification problem 2
14. International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020
14
Table 7:Performance comparison of IT2IFLS on system identification problem 2 with other models in the
literature
Figure 8: Actual and predicted outputsfor system identification problem 2
Figure 7 shows the actual and predicted output for system identification problem 2. As shown in
the figure, the predicted output closely follows the actual output indicating a good learning
performance. As shown in Table 7, IT2IFLS-SMC performs well in this problem instance in
terms of the test RMSE compared to other existing models in the literature.
7. CONCLUSION
This paper analysis time series and system identification problems using IT2IFLS trained with
SMC learning algorithm. As demonstrated in the experimental studies, IT2IFLS-SMC
outperforms some existing studies in the literature and competes favourably with others in some
problem instances in terms of the test RMSE. For the training RMSE, IT2IFLS-SMC performs
poorly in many cases. A careful look at all the Australian seasons prediction, IT2IFLS-SMC
performs poorly compared to other learning models in terms of the training RMSE.
15. International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020
15
In the future, it will be worthwhile to look at using other derivative-free learning algorithms and
possibly hybridize them and evaluate their predictive strengths. It will also be interesting to
consider other membership functions outside the conventional membership functions such as
elliptic and diamond-shaped membership functions for interval type-2 intuitionistic fuzzy set.
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AUTHORS
Eyoh, Imo Jeremiah received the B.Sc. and M.Sc. degrees in Computer Science
from the University of Uyo and Ibadan, all in Nigeria in 1999 and 2005
respectively. She obtained her PhD in Computer Science from the University of
Nottingham, United Kingdom in 2018. She is currently working in the Department
of Computer Science, University of Uyo, as a Senior lecturer. Her main research
interest is uncertainty modelling using fuzzy logic. In particular, she works in the
area of type-2 fuzzy logic (classical and intuitionistic) with application to diverse
problem domains. She has published many papers in reputable national and international journals and
conferences. She is a member of Computer Professionals Registration Council of Nigeria (CPN) and
Organisation for Women in Science for the Developing World (OWSD). She is also a member of IEEE.
Eyoh, Jeremiah Effiong received the B.Sc in Computer Engineering from the
University of Uyo in 2006, Nigeria. He received the M.Sc in Control Engineering
from the University of Sheffield, United Kingdom in 2008. He is currently working
towards the Ph.D. degree in Electrical, Electronics and Systems Engineering at the
Loughborough University, United Kingdom.
Short Biography