Genetic algorithms for the elimination of redundancy and/or rule contribution assessment in fuzzy models

Takagi-Sugeno fuzzy models may contain redundant rules. The use of genetic algorithms for optimizing a performance index, which combines a modelling error and the number of rules in the model, allows the elimination of redundant rules and a subsequent adjustment of the weights of the rules retained in the model. The method is illustrated by examples.

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Year of publication:	1996
Authors:	Zhao, J. ; Gorez, R. ; Wertz, V.
Published in:	Mathematics and Computers in Simulation (MATCOM). - Elsevier, ISSN 0378-4754. - Vol. 41.1996, 1, p. 139-148
Publisher:	Elsevier

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010870488