Exploratory Fuzzy Modeling

Abstract

We proposed a clustering algorithm to create a balance between the continuity and the linearity of data distribution within clusters. The purpose of this clustering is to find linear substructures of the system under study in order to build a fuzzy implication inference model proposed by Takagi and Sugeno. The technical proposal in this paper is related to the integration of rules : selection of conditional variables, identification of membership functions, and evaluation of the obtained fuzzy model. A concrete example presented is a macro model that can predict water quality of small rivers in the Tokyo metropolitan area under certain scenarios of human activity.