NDLI: Identification of Takagi-Sugeno (TS) fuzzy model with Evolutionary Parallel Gradient Search

Content Provider	IEEE Xplore Digital Library
Author	Zhao Zhongyu Wenfang Xie Herry Hong
Copyright Year	2008
Description	Author affiliation: Dept. of Mech. & Ind. Eng., Concordia Univ., Montreal, QC (Zhao Zhongyu; Wenfang Xie; Herry Hong)
Abstract	In this paper the modeling of nonlinear system with TS fuzzy model is discussed. The identification of TS fuzzy model is first posed as an optimization problem and a new hybrid optimization algorithm- referred to as evolutionary parallel gradient search (EPGS) is applied to find the optimal values of the parameters in the fuzzy model. The main feature of EPGS is its ability to deal with the local minima problem in global optimization. By using the gradient information of cost function, EPGS combines gradient-based algorithm and evolutionary algorithm (EA) in an innovative way such that EA is used to keep the best searches at every step in the optimization process and the gradient descent method is used to update these best searches. The application of EPGS in the parameter estimation problem of TS fuzzy models shows excellent performance in terms of modeling accuracy.
Starting Page	1
Ending Page	6
File Size	299334
Page Count	6
File Format	PDF
ISBN	9781424423514
DOI	10.1109/NAFIPS.2008.4531203
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Takagi-Sugeno model Fuzzy systems Parameter estimation Optimization methods Cost function Evolutionary computation Nonlinear systems Fuzzy sets Convergence Search methods TS fuzzy modeling Evolutionary Algorithm Gradient search
Content Type	Text
Resource Type	Article

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