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

J. Zhao, R. Gorez and V. Wertz

Mathematics and Computers in Simulation (MATCOM), 1996, vol. 41, issue 1, 139-148

Abstract: 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.

Date: 1996
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:41:y:1996:i:1:p:139-148

DOI: 10.1016/0378-4754(95)00066-6

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