Rules Extraction of Interval Type-2 Fuzzy Logic System Based on Fuzzy c-Means Clustering

Abstract

An improved clustering algorithm is proposed in this paper, which originates from Fuzzy c-Means Clustering(FCM). FCM is one of the algorithms used commonly to extract fuzzy rules from type-1 fuzzy logic system. However, its application is merely limited to dots set. This deficiency is improved in the new algorithm, Interval Fuzzy c-Means Clustering(IFCM), which is adequate to deal with interval sets. The enhanced algorithm is based on a new definition of distance between interval data. This article will also focus on extracting fuzzy rule from interval type-2 fuzzy systems. The type-2 fuzzy system is suitable to handle the situations with complicated uncertainties. However, how to extract fuzzy rules from type-2 fuzzy logic systems remains an important issue. This paper will attempt to exhibit an unique method to extract rule from interval type-2 fuzzy systems with IFCM. Simulation results are included at the end of this article that indicates the validity of IFCM.