NDLI: Evolutionary fuzzy modeling using fuzzy neural networks and genetic algorithm

Content Provider	IEEE Xplore Digital Library
Author	Furuhashi, T. Matsushita, S. Tsutsui, H.
Copyright Year	1997
Description	Author affiliation: Dept. of Inf. Electron., Nagoya Univ., Japan (Furuhashi, T.)
Abstract	Fuzzy modeling is one of the promising methods for describing nonlinear systems. The determination of the antecedent structure of the fuzzy model, i.e. input variables and the number of membership functions for the inputs, has been one of the most important problems of fuzzy modeling. The authors propose a hierarchical fuzzy modeling method using fuzzy neural networks (FNN) and a genetic algorithm (GA). This method can identify fuzzy models of nonlinear objects with strong nonlinearities. The disadvantage of this method is that the training of the FNN is time consuming. This paper presents a quick method for rough search for proper structures in the antecedent of fuzzy models. The fine tuning of the acquired rough model is done by the FNNs. This modeling method is quite efficient to identify precise fuzzy models of systems with strong nonlinearities. A simulation is done to show the effectiveness of the proposed method.
Starting Page	623
Ending Page	627
File Size	419014
Page Count	5
File Format	PDF
ISBN	0780339495
DOI	10.1109/ICEC.1997.592387
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-04-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Genetic algorithms Input variables Fuzzy systems Fuzzy sets Nonlinear systems Abstracts Data mining Data handling Poles and towers
Content Type	Text
Resource Type	Article

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