Extracting Fuzzy Rules from Data for Function Approximation and Pattern Classification

Abstract

Extracting fuzzy rules from data allows relationships in the data to be modeled by if-then rules that are easy to understand, verify, and extend. This paper presents methods for extracting fuzzy rules for both function approximation and pattern classification. The rule extraction methods are based on estimating clusters in the data; each cluster obtained corresponds to a fuzzy rule that relates a region in the input space to an output region (or, in the case of pattern classification, to an output class). After the number of rules and initial rule parameters are obtained by cluster estimation, the rule parameters are optimized by gradient descent. Applications to a function approximation problem and to a pattern classification problem are also illustrated.