FUZZY INFERENCE MODELING BASED ON FUZZY SINGLETON-TYPE REASONING

In this paper, the idea of the neuro-fuzzy learning algorithm has been ex- tended, by which the tuning parameters in the fuzzy rules can be learned without changing the fuzzy rule table form used in usual fuzzy applications. A new neuro-fuzzy learning algorithm in the case of the fuzzy singleton-type reasoning method has been proposed. Due to the flexibility of the fuzzy singleton-type reasoning method, the extended method is more reasonable and suitable for constructing an optimum fuzzy system model than the conventional neuro-fuzzy learning algorithm. Moreover, the efficiency of the extended neuro-fuzzy learning algorithm compared to a genetic algorithm is demonstrated by iden- tifying a nonlinear function.

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