Efficient Self-Evolving Evolutionary Learning for Neurofuzzy Inference Systems

Authors:
Cheng-Jian Lin;Cheng-Hung Chen;Chin-Teng Lin
Affiliations:
Dept. of Comput. Sci. & Inf. Eng., Nat. Chin-Yi Univ., Taiping;-;-
Venue:
IEEE Transactions on Fuzzy Systems
Year:
2008

Citing 0
Cited 8

eFSM: a novel online neural-fuzzy semantic memory model

IEEE Transactions on Neural Networks
A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing

Fuzzy Sets and Systems
Variational bayes for a mixed stochastic/deterministic fuzzy filter

IEEE Transactions on Fuzzy Systems
Fuzzy wavelet neural network models for prediction and identification of dynamical systems

IEEE Transactions on Neural Networks
Genetic regulatory network-based symbiotic evolution

Expert Systems with Applications: An International Journal
Optimization of fuzzy systems using group-based evolutionary algorithm

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Efficient differential evolution algorithm-based optimisation of fuzzy prediction model for time series forecasting

International Journal of Intelligent Information and Database Systems
Efficient differential evolution algorithm-based optimisation of fuzzy prediction model for time series forecasting

International Journal of Intelligent Information and Database Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This study proposes an efficient self-evolving evolutionary learning algorithm (SEELA) for neurofuzzy inference systems (NFISs). The major feature of the proposed SEELA is that it is based on evolutionary algorithms that can determine the number of fuzzy rules and adjust the NFIS parameters. The SEELA consists of structure learning and parameter learning. The structure learning attempts to determine the number of fuzzy rules. A subgroup symbiotic evolution is adopted to yield several variable fuzzy systems, and an elite-based structure strategy is adopted to find a suitable number of fuzzy rules for solving a problem. The parameter learning is to adjust parameters of the NFIS. It is a hybrid evolutionary algorithm of cooperative particle swarm optimization (CPSO) and cultural algorithm, called cultural CPSO (CCPSO). The CCPSO, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Experimental results demonstrate that the proposed method performs well in predicting time series and solving nonlinear control problems.