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Fuzzy Identification of Nonlinear Systems via Orthogonal Transform
Author(s) -
Wang Hongwei,
Wang Jia,
Gu Hong
Publication year - 2012
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.404
Subject(s) - defuzzification , fuzzy logic , fuzzy set operations , fuzzy associative matrix , neuro fuzzy , fuzzy classification , mathematics , fuzzy number , identification (biology) , fuzzy rule , fuzzy control system , artificial intelligence , algorithm , computer science , control theory (sociology) , fuzzy set , control (management) , botany , biology
Abstract This paper is concerned with the application of orthogonal transforms and fuzzy competitive learning to extract fuzzy rules from data. The least square algorithm with orthogonal transforms is proposed to supervise the progress of fuzzy competitive learning. First of all, competitive learning takes place in the product space of system inputs and outputs and each cluster corresponds to a fuzzy IF–THEN rule. The fuzzy relation matrix, confirmed by fuzzy competitive learning, is studied by the orthogonal least square algorithm. The validity of fuzzy rules is obtained by analyzing the effect of orthogonal vectors in the fuzzy model, and subsequently removing less important ones. The structure identification and parameter identification of the fuzzy model are simultaneously confirmed in the proposed algorithm. Using simulation results as an example, the fuzzy model of non‐linear systems can be built by using the proposed algorithm. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society