Automatic fuzzy rules production based on clustering and implication selection

Abstract

This paper deals with improving the approximation capability of fuzzy systems. Fuzzy negations produced via conical sections are a promising methodology towards better fuzzy implications in fuzzy rules. The linguistic variables and the fuzzy rules are induced automatically following a fuzzy equivalence relation. The uncertainty of linear or nonlinear systems is thus dealt with. In this study, the clustering is optimized without human intervention, but also the best inference mechanism for a particular dataset is prescribed. It has been found that clustering based on fuzzy equivalence relation and fuzzy inference via conical sections leads to remarkably accurate approximations. A fuzzy rule based system with fewer control parameters is proposed. An application on telecom data shows the use of the methodology, its applicability to a real problem and its performance compared to other alternatives in terms of quality.