NDLI: Modified Gath-Geva clustering based linear T-S fuzzy model identification for multi-input multi-output chaotic systems

Content Provider	IEEE Xplore Digital Library
Author	Liu Yu-Yan Zhou Shi-Liang
Copyright Year	2012
Description	Author affiliation: School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206 (Zhou Shi-Liang) \|\| School of Nuclear Science and Engineering, North China Electric Power University, Beijing, 102206 (Liu Yu-Yan)
Abstract	A modified Gath-Geva clustering based linear T-S fuzzy model identification method for multi-input multi-output chaotic systems is proposed. Noise reduction via local projection and derivatives of state variable estimation using Heirmit spline interpolation are performed at first for better data. Then initial linear T-S fuzzy model is derived using modified Gath-Geva clustering, whose rule number is optimized by clustering validation procedure. After clustering, model reduction is implemented using OLS (Orthogonal Least Squares) and modified Fischer's interclass separability criteria in order to obtain compact and transparent model. Parameters of the reduced fuzzy model are optimized by constraint Levenberg-Marquardt method to improve its precision, while preserving its interpretability. Simulation results show the efficiency of proposed method.
Starting Page	1771
Ending Page	1776
File Size	1831632
Page Count	6
File Format	PDF
ISBN	9781467325813
ISSN	21612927
e-ISBN	9789881563811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Integrated circuits Chaos Computational modeling fuzzy clustering model reduction Educational institutions Reduced order systems Electronic mail Power systems linear T-S fuzzy model chaotic system
Content Type	Text
Resource Type	Article

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