Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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APPLICATION OF GENETIC-BASED FUZZY INFERENCE TO FUZZY CONTROL

  • Park, Daihee (Department of Computer Science, Florida State University) ;
  • Kandel, Abraham (Department of Computer Science and Engineering, University of South Florida) ;
  • Langholz, Gideon (Department of Electrical Engineering, Florida State University)
  • Published : 1992.06.30
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Abstract

The successful application of fuzzy reasoning models to fuzzy control systems depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. It is shown ill this paper that the performance of fuzzy control systems call be improved if the fuzzy reasoning model is supplemented by a genetic-based learning mechanism. The genetic algorithm enables us to generate all optimal set of parameters for the fuzzy reasoning model based either on their initial subjective selection or on a random selection. It is shown that if knowledge of the domain is available, it is exploited by the genetic algorithm leading to an even better performance of the fuzzy controller.

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