Learning of Fuzzy Rules by Mountain Clustering

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.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.