Abstract

This paper presents an approach which is useful for the identification of discrete dynamic systems based on fuzzy relational models. If the number of input variables and fuzzy sets increases, a fuzzy system gets increasingly intractable. A concept based on the decomposition of multivariable rule-base is presented. Two decomposed fuzzy models based on the simplified inference break up method are proposed and applied to a dynamic systems modelling. Evolution of identification algorithms for the decomposed fuzzy model is suggested. A comparative study of the dynamic system identification with the conventional relational model and the decomposed relational model is presented for Box-Jenkins data.

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