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

✍ Scribed by J. Zhao; R. Gorez; V. Wertz

• Publisher: Elsevier Science
• Year: 1996
• Tongue: English
• Weight: 581 KB
• Volume: 41
• Category: Article
• ISSN: 0378-4754
• DOI: 10.1016/0378-4754(95)00066-6

✦ Synopsis

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.