Using inductive learning to determine fuzzy rules for dynamic systems

Abstract

This paper describes a fuzzy model-building methodology. Process input-output data are quantized by fuzzy reference membership functions. Membership values are determined for each data point and only the membership function that generates the maximum membership value is used. The name of membership functions corresponding to maximum values are fed into an inductive learning algorithm and the fuzzy rules are determined. The methodology is applied to fuzzy modeling of a dynamic system.