A simple but powerful heuristic method for generating fuzzy rules from numerical data

Abstract

In this paper, we propose a simple but powerful heuristic method for automatically generating fuzzy if-then rules from numerical data. Fuzzy if-then rules with nonfuzzy singletons (i.e., real numbers) in the consequent parts are generated by the proposed heuristic method. The main advantage of the proposed heuristic method is its simplicity, i.e., it involves neither time-consuming iterative learning procedures nor complicated rule generation mechanisms. We also suggest a linguistic representation method for deriving linguistic rules from fuzzy if-then rules with consequent real numbers. The proposed linguistic approximation method consists of two linguistic rule tables, which can realize exactly the same nonlinear mapping as an original system based on fuzzy if-then rules with consequent real numbers. Using computer simulations on rice taste data, we demonstrate the high performance of the proposed heuristic method and illustrate the proposed linguistic representation method.