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In designing a fuzzy model and a fuzzy controller, we encounter a major difficulty in the identification of an optimised fuzzy rule base, which is traditionally achieved by a tedious trial and error process. The paper presents an approach to automatic design of optimal fuzzy rule bases for modelling and control using evolutionary programming. Such programming simultaneously evolves the structure and the parameter of fuzzy rule base for a given task. To check the effectiveness of the suggested approach, five examples for modelling and control are examined. The performance of the identified fuzzy models and fuzzy controllers is demonstrated.
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Han Sung Hwang (1999) studied this question.
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