Fuzzy Clustering with Selection of Premise Variables for Identification of Fuzzy Models

Abstract

This paper proposes a fuzzy modeling technique taking into account selection of premise variables and the number of fuzzy rules. This technique is effective for a large-scale data set and of a large number of variables. In fuzzy modeling, a fuzzy clustering method is also proposed in order to determine a data partition and consequence parameters simultaneously under the assumed consequence variables. The proposed criterion of the fuzzy clustering has two purposes to detect the linearity in the space of consequence variables and the continuity in the space of premisse variables.