Learning of fuzzy rules by mountain clustering

Ronald R. Yager; Dimitar P. Fileu

doi:10.1117/12.165030

22 December 1993 Learning of fuzzy rules by mountain clustering

Ronald R. Yager, Dimitar P. Fileu

Proceedings Volume 2061, Applications of Fuzzy Logic Technology; (1993) https://doi.org/10.1117/12.165030
Event: Optical Tools for Manufacturing and Advanced Automation, 1993, Boston, MA, United States

Abstract

The paper deals with a new approach to the learning of fuzzy rules. It suggests a solution to one of the problems of crucial importance for the learning of fuzzy rules by back propagation- -the issue of estimation of the initial values of the unknown parameters. We introduce the method of clustering via the mountain function to identify the most important rules. Those are the rules that are associated with higher values of the peaks of the mountain function. From the centers of the clusters that are obtained by the mountain function method are determined the initial estimates of the parameters of the reference antecedent and consequent fuzzy sets of the rules. In the next step the method of back propagation is used for more precise identification of those parameters.

Citation Download Citation

Ronald R. Yager and Dimitar P. Fileu "Learning of fuzzy rules by mountain clustering", Proc. SPIE 2061, Applications of Fuzzy Logic Technology, (22 December 1993); https://doi.org/10.1117/12.165030

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