Fuzzy Modeling by Occupancy Degree and Optimal Partition of Projection Using Rough Set Theory

Abstract

The rough set theory suggested by Pawlak represents the degree of consistency between conditions and decision attributes of data pairs that have no linguistic information. In this paper, by using this representation feature, we define a measure called the occupancy degree that represents the consistency degree of a premise and consequent variables in fuzzy rules describing experimental data pairs. We also propose a method by which we partition the projected data on input space and find an optimal fuzzy rule table and membership functions of input and output variables from data without preliminary linguistic information. We examine the validity of the proposed method by modeling data pairs randomly generated by a fuzzy system.