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

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.