A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing

Authors:
Chia-Feng Juang;Yang-Yin Lin;Chiu-Chuan Tu
Affiliations:
Department of Electrical Engineering, National Chung Hsing University, Taichung 402, Taiwan, ROC;Department of Electrical Engineering, National Chung Hsing University, Taichung 402, Taiwan, ROC;Department of Electrical Engineering, National Chung Hsing University, Taichung 402, Taiwan, ROC
Venue:
Fuzzy Sets and Systems
Year:
2010

Citing 16
Cited 1

Nonlinear system identification with recurrent neural networks and dead-zone Kalman filter algorithm

Neurocomputing
Automatic construction of feedforward/recurrent fuzzy systems by clustering-aided simplex particle swarm optimization

Fuzzy Sets and Systems
A high-order recurrent neuro-fuzzy system with internal dynamics: Application to the adaptive noise cancellation

Fuzzy Sets and Systems
Genetic fuzzy learning

IEEE Transactions on Evolutionary Computation
Dynamic fuzzy neural networks-a novel approach to functionapproximation

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A recurrent fuzzy-neural model for dynamic system identification

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Direct adaptive iterative learning control of nonlinear systems using an output-recurrent fuzzy neural network

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Prediction and identification using wavelet-based recurrent fuzzy neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An online self-constructing neural fuzzy inference network and its applications

IEEE Transactions on Fuzzy Systems
Identification and control of dynamic systems using recurrent fuzzy neural networks

IEEE Transactions on Fuzzy Systems
A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms

IEEE Transactions on Fuzzy Systems
Efficient Self-Evolving Evolutionary Learning for Neurofuzzy Inference Systems

IEEE Transactions on Fuzzy Systems
Recurrent neuro-fuzzy networks for nonlinear process modeling

IEEE Transactions on Neural Networks
A recurrent self-organizing neural fuzzy inference network

IEEE Transactions on Neural Networks
Subsethood-product fuzzy neural inference system (SuPFuNIS)

IEEE Transactions on Neural Networks
Hierarchical Singleton-Type Recurrent Neural Fuzzy Networks for Noisy Speech Recognition

IEEE Transactions on Neural Networks

An enhanced discriminability recurrent fuzzy neural network for temporal classification problems

Fuzzy Sets and Systems

Quantified Score

Hi-index	0.20

Visualization

Abstract

This paper proposes a recurrent self-evolving fuzzy neural network with local feedbacks (RSEFNN-LF) for dynamic system processing. A RSEFNN-LF is composed of zero-order or first-order Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy if-then rules. The recurrent structure in a RSEFNN-LF comes from locally feeding the firing strength of a fuzzy rule back to itself. A RSEFNN-LF is constructed on-line via simultaneous structure and parameter learning. In structure learning, an efficient rule and fuzzy set generation algorithm is proposed to generate fuzzy rules on-line and reduce the number of fuzzy sets in each dimension. In parameter learning, the consequent part parameters are learned through a varying-dimensional Kalman filter algorithm whose input dimension varies with structure learning. The antecedent part and feedback loop parameters are learned using a gradient descent algorithm. The RSEFNN-LF is applied to dynamic system identification, chaotic sequence prediction, and speech recognition problems. This paper also compares the performance of the RSEFNN-LF with other recurrent fuzzy neural networks.