Abstract
This paper proposes a GA and GDM-based method for removing the unnecessary rules and generating the relevant rules from the fuzzy rules corresponding to several fuzzy partitions. The aim of the proposed method is to find a minimum set of fuzzy rules that can correctly classify all the training patterns. This is achieved by formulating and solving a combinatorial optimization problem that has two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy rules. The fuzzy inference is structured by a set of simple fuzzy rules. In each rule, the antecedent part is made up of the membership functions of a fuzzy set, and the consequent part is made up of a real number. The membership functions and the number of fuzzy inference rules are tuned by means of the GA, while the real numbers in the consequent parts of the rules are tuned by means of the gradient descent method. In order to prove the effectiveness of the proposed method, computer simulation results are shown.
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