Systematic rule reduction of a multi-stage fuzzy logic model

Abstract

A multi-stage fuzzy logic model is systematically reduced to obtain a significantly smaller rulebase. The multi-stage structure is obtained by unfolding a single-stage, n-dimension fuzzy logic model into multiple, two-dimension stages. The interconnection between stages is not defuzzified. Rule reduction is performed by comparing output membership functions in the final two-dimension rulebase, weighted by the amount of use each rule receives, called the sum of truth, from the previous stage. The method is demonstrated on a Mackey Glass series and on a two-link robot, both with encouraging results.