DocumentCode :
1365926
Title :
Linguistic fuzzy model identification
Author :
Hwang, H.-S. ; Woo, K.B.
Author_Institution :
Dept. of Electr. Eng., Yonsei Univ., Seoul, South Korea
Volume :
142
Issue :
6
fYear :
1995
fDate :
11/1/1995 12:00:00 AM
Firstpage :
537
Lastpage :
544
Abstract :
The paper presents an approach for identifying a fuzzy model composed of fuzzy-logic based linguistic rules for a multi-input/single-output system. The approach includes structure identification and parameter identification. We propose to utilise a fuzzy c-means clustering and genetic algorithm (GA) hybrid scheme to identify the structure and the parameters of a fuzzy model, respectively. To evaluate the advantages and the effectiveness of the suggested approach, we deal with numerical examples. Comparison shows that the proposed approach can produce the fuzzy model with higher accuracy and a smaller number of rules than previously achieved in other works. To show the global optimisation and local convergence of the GA hybrid scheme, we also consider an optimisation problem having a few local minima and maxima
Keywords :
convergence of numerical methods; fuzzy logic; fuzzy set theory; genetic algorithms; identification; multivariable systems; MISO systems; fuzzy c-means clustering; fuzzy-logic; genetic algorithm; global optimisation; linguistic fuzzy model; linguistic rules; local convergence; parameter identification; structure identification;
fLanguage :
English
Journal_Title :
Control Theory and Applications, IEE Proceedings -
Publisher :
iet
ISSN :
1350-2379
Type :
jour
DOI :
10.1049/ip-cta:19952254
Filename :
668933
Link To Document :
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