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K Subramanian - One of the best experts on this subject based on the ideXlab platform.
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an evolving interval type 2 neurofuzzy Inference System and its metacognitive sequential learning algorithm
IEEE Transactions on Fuzzy Systems, 2015Co-Authors: A K Das, K Subramanian, Suresh SundaramAbstract:In this paper, we propose an evolving interval type-2 neurofuzzy Inference System (IT2FIS) and its fully sequential learning algorithm. IT2FIS employs interval type-2 fuzzy sets in the antecedent part of each rule and the consequent realizes Takagi–Sugeno–Kang fuzzy Inference mechanism. In order to render the Inference fast and accurate, we propose a data-driven interval-reduction approach to convert interval type-1 fuzzy set in antecedent to type-1 fuzzy number in the consequent. During learning, the sequential algorithm learns a sample one-by-one and only once. The IT2FIS structure evolves automatically and adapts its network parameters using metacognitive learning mechanism concurrently. The metacognitive learning regulates the learning process by appropriate selection of learning strategies and helps the proposed IT2FIS to approximate the input–output relationship efficiently. An evolving IT2FIS employing a metacognitive learning algorithm is referred to as McTI2FIS. Performance of metacognitive interval type-2 neurofuzzy Inference System (McIT2FIS) is evaluated using a set of benchmark time-series problems and is compared with existing type-2 and type-1 fuzzy Inference Systems. Finally, the performance of the proposed McIT2FIS has been evaluated using a practical stock price-tracking problem. The results clearly highlight that McIT2FIS performs better than other existing results in the literature.
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a complex valued neuro fuzzy Inference System and its learning mechanism
Neurocomputing, 2014Co-Authors: K Subramanian, R Savitha, S SureshAbstract:In this paper, we present a complex-valued neuro-fuzzy Inference System (CNFIS) and develop its meta-cognitive learning algorithm. CNFIS has four layers - an input layer with m rules, a Gaussian layer with K rules, a normalization layer with K rules and an output layer with n rules. The rules in the Gaussian layer map the m-dimensional complex-valued input features to a K-dimensional real-valued space. Hence, we use the Wirtinger calculus to obtain the complex-valued gradients of the real-valued function in deriving the learning algorithm of CNFIS. Next, we also develop the meta-cognitive learning algorithm for CNFIS referred to as ''meta-cognitive complex-valued neuro-fuzzy Inference System (MCNFIS)''. CNFIS is the cognitive component of MCNFIS and a self-regulatory learning mechanism that decides what-to-learn, how-to-learn, and when-to-learn in a meta-cognitive framework is its meta-cognitive component. Thus, for every epoch of the learning process, the meta-cognitive component decides if each sample in the training set must be deleted or used to update the parameters of CNFIS or to be reserved for future use. The performances of CNFIS and MCNFIS are studied on a set of approximation and real-valued classification problems, in comparison to existing complex-valued learning algorithms in the literature. First, we evaluate the approximation performances of CNFIS and MCNFIS on a synthetic complex-valued function approximation problem, an adaptive beam-forming problem and a wind prediction problem. Finally, we study the decision making performance of CNFIS and MCNFIS on a set of benchmark real-valued classification problems from the UCI machine learning repository. Performance study results on approximation and real-valued classification problems show that CNFIS and MCNFIS outperform existing algorithms in the literature.
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a meta cognitive sequential learning algorithm for neuro fuzzy Inference System
Applied Soft Computing, 2012Co-Authors: K Subramanian, S SureshAbstract:In this paper, we present a meta-cognitive sequential learning algorithm for a neuro-fuzzy Inference System, referred to as, 'Meta-Cognitive Neuro-Fuzzy Inference System' (McFIS). McFIS has two components, viz., a cognitive component and a meta-cognitive component. The cognitive component employed is a Takagi-Sugeno-Kang type-0 neuro-fuzzy Inference System. A self-regulatory learning mechanism that controls the learning process of the cognitive component, by deciding what-to-learn, when-to-learn and how-to-learn from sequential training data, forms the meta-cognitive component. McFIS realizes the above decision by employing sample deletion, sample reserve and sample learning strategy, respectively. The meta-cognitive component use the instantaneous error of the sample and spherical potential of the rule antecedents to select the best training strategy for the current sample. Also, in sample learning strategy, when a new rule is added the rule consequent is assigned such that the localization property of Gaussian rule is fully exploited. The performance of McFIS is evaluated on four regression and eight classification problems. The performance comparison shows the superior generalization performance of McFIS compared to existing algorithms.
S Suresh - One of the best experts on this subject based on the ideXlab platform.
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a complex valued neuro fuzzy Inference System and its learning mechanism
Neurocomputing, 2014Co-Authors: K Subramanian, R Savitha, S SureshAbstract:In this paper, we present a complex-valued neuro-fuzzy Inference System (CNFIS) and develop its meta-cognitive learning algorithm. CNFIS has four layers - an input layer with m rules, a Gaussian layer with K rules, a normalization layer with K rules and an output layer with n rules. The rules in the Gaussian layer map the m-dimensional complex-valued input features to a K-dimensional real-valued space. Hence, we use the Wirtinger calculus to obtain the complex-valued gradients of the real-valued function in deriving the learning algorithm of CNFIS. Next, we also develop the meta-cognitive learning algorithm for CNFIS referred to as ''meta-cognitive complex-valued neuro-fuzzy Inference System (MCNFIS)''. CNFIS is the cognitive component of MCNFIS and a self-regulatory learning mechanism that decides what-to-learn, how-to-learn, and when-to-learn in a meta-cognitive framework is its meta-cognitive component. Thus, for every epoch of the learning process, the meta-cognitive component decides if each sample in the training set must be deleted or used to update the parameters of CNFIS or to be reserved for future use. The performances of CNFIS and MCNFIS are studied on a set of approximation and real-valued classification problems, in comparison to existing complex-valued learning algorithms in the literature. First, we evaluate the approximation performances of CNFIS and MCNFIS on a synthetic complex-valued function approximation problem, an adaptive beam-forming problem and a wind prediction problem. Finally, we study the decision making performance of CNFIS and MCNFIS on a set of benchmark real-valued classification problems from the UCI machine learning repository. Performance study results on approximation and real-valued classification problems show that CNFIS and MCNFIS outperform existing algorithms in the literature.
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a meta cognitive sequential learning algorithm for neuro fuzzy Inference System
Applied Soft Computing, 2012Co-Authors: K Subramanian, S SureshAbstract:In this paper, we present a meta-cognitive sequential learning algorithm for a neuro-fuzzy Inference System, referred to as, 'Meta-Cognitive Neuro-Fuzzy Inference System' (McFIS). McFIS has two components, viz., a cognitive component and a meta-cognitive component. The cognitive component employed is a Takagi-Sugeno-Kang type-0 neuro-fuzzy Inference System. A self-regulatory learning mechanism that controls the learning process of the cognitive component, by deciding what-to-learn, when-to-learn and how-to-learn from sequential training data, forms the meta-cognitive component. McFIS realizes the above decision by employing sample deletion, sample reserve and sample learning strategy, respectively. The meta-cognitive component use the instantaneous error of the sample and spherical potential of the rule antecedents to select the best training strategy for the current sample. Also, in sample learning strategy, when a new rule is added the rule consequent is assigned such that the localization property of Gaussian rule is fully exploited. The performance of McFIS is evaluated on four regression and eight classification problems. The performance comparison shows the superior generalization performance of McFIS compared to existing algorithms.
Engin Avci - One of the best experts on this subject based on the ideXlab platform.
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an expert discrete wavelet adaptive network based fuzzy Inference System for digital modulation recognition
Expert Systems With Applications, 2007Co-Authors: Engin Avci, Davut Hanbay, Asaf VarolAbstract:This paper presents a comparative study of implementation of feature extraction and classification algorithms based on discrete wavelet decompositions and Adaptive Network Based Fuzzy Inference System (ANFIS) for digital modulation recognition. Here, in first stage, 20 different feature extraction methods are generated by separately using Daubechies, Biorthogonal, Coiflets, Symlets wavelet families. In second stage, the performance comparison of these feature extraction methods is performed by using a new Expert Discrete Wavelet Adaptive Network Based Fuzzy Inference System (EDWANFIS). The digital modulated signals used in this experimental study are ASK8, FSK8, PSK8, QASK8. EDWANFIS structure consists of two parts. The first part is Discrete Wavelet Transform (DWT)-adaptive wavelet entropy and Adaptive Network Based Fuzzy Inference System for Automatic Digital Modulation Recognition (ADMR). The performance of this comparison System is evaluated by using total 800 digital modulated signals for each of these feature extraction methods. The performance comparison of these features extraction methods and the advantages and disadvantages of the methods are examined.
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speech recognition using a wavelet packet adaptive network based fuzzy Inference System
Expert Systems With Applications, 2006Co-Authors: Engin Avci, Zuhtu Hakan AkpolatAbstract:Abstract In this paper, an expert speech recognition System is presented. This paper especially deals with the combination of feature extraction and classification for real speech signals. A Wavelet packet adaptive network based fuzzy Inference System (WPANFIS) model is developed in this study. WPANFIS consists of two layers: wavelet packet and adaptive network based fuzzy Inference System. The wavelet packet layer is used for adaptive feature extraction in the time–frequency domain and is composed of wavelet packet decomposition and wavelet packet entropy. The performance of the developed System is evaluated by using noisy speech signals. Test results showing the effectiveness of the proposed speech recognition System are presented in the paper. The rate of correct classification is about 92% for the sample speech signals.
Hossein Bonakdari - One of the best experts on this subject based on the ideXlab platform.
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performance evaluation of adaptive neural fuzzy Inference System for sediment transport in sewers
Water Resources Management, 2014Co-Authors: Isa Ebtehaj, Hossein BonakdariAbstract:The application of models capable of estimating sediment transport in sewers has been a frequent practice in the past years. Considering the fact that predicting sediment transport within the sewer is a complex phenomenon, the existing equations used for predicting densimetric Froude number do not present similar results. Using Adaptive Neural Fuzzy Inference System (ANFIS) this article studies sediment transport in sewers. For this purpose, five different dimensionless groups including motion, transport, sediment, transport mode and flow resistance are introduced first and then the effects of various parameters in different groups on the estimation of the densimetric Froude number in the motion group are presented as six different models. To present the models, two states of grid partitioning and sub-clustering were used in Fuzzy Inference System (FIS) generation. Moreover, the training algorithms applied in this article include back propagation and hybrid. The results of the proposed models are compared with the experimental data and the existing equations. The results show that ANFIS models have greater accuracy than the existing sediment transport equations.
Meichiu Pan - One of the best experts on this subject based on the ideXlab platform.
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using adaptive network based fuzzy Inference System to forecast regional electricity loads
Energy Conversion and Management, 2008Co-Authors: Lichih Ying, Meichiu PanAbstract:Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy Inference System (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy Systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads.
