NDLI: Constructing a kind of fuzzy systems based on neural networks techniques

Content Provider	IEEE Xplore Digital Library
Author	Ming Qing Hai-Liang Zhao Shi-Fen Xia Xue-Fang Wang
Copyright Year	2003
Description	Author affiliation: Dept. of Math., Southwest Jiaotong Univ., Sichuan, China (Ming Qing; Hai-Liang Zhao; Shi-Fen Xia; Xue-Fang Wang)
Abstract	In this paper, a method to model a kind of nonlinear fuzzy system with fuzzy border is presented. BP networks (BPN) possess efficient capability of approximating nonlinear function and radius base function networks (RBFN) have the fast training speed. These advantages of BPN and RBFN can be combined with clustering techniques to improve system modeling. Firstly, the system structure is obtained by clustering. Secondly the BPN is employed to generate rule base's antecedent function and RBFN to approximate each rule's conclusion function, respectively. So the initial construction of the system can be acquired. Thirdly, structure design and training of networks are discussed in detail. Finally, the structure optimization and overstudy of RBFN are discussed.
Sponsorship	IEEE Systems Man and Cybernetics Tech. Committee on Cybernetics, Hebei Univ. Northwestern Polytechnical Univ
Starting Page	2629
Ending Page	2633
File Size	257458
Page Count	5
File Format	PDF
ISBN	0780381319
DOI	10.1109/ICMLC.2003.1259974
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy systems Neural networks Function approximation Mathematics Electronic mail Mathematical model Modeling Radial basis function networks Control system synthesis Fuzzy control
Content Type	Text
Resource Type	Article

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