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Trainable fuzzy classification systems based on fuzzy if-then rules

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3 Author(s)
Nozaki, K. ; Dept. of Ind. Eng., Osaka Prefecture Univ., Japan ; Ishibuchi, H. ; Tanaka, H.

This paper proposes a learning method of fuzzy classification systems based on fuzzy if-then rules for subsequently modifying the grade of certainty of each fuzzy if-then rule by an error-correction learning rule. To illustrate the proposed method, we apply it to a two-class classification problem in a two-dimensional pattern space. To evaluate the performance of the proposed method, we also apply it to the iris data of Fisher. Since the learning by the proposed method is stopped when all training patterns are correctly classified, we also suggest an additional learning method that is not based on the error-correction learning rule

Published in:

Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on

Date of Conference:

26-29 Jun 1994

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