Fuzzy Logic Controller Based on Association Rules

Abstract

The task of the standard Mamdani fuzzy logic controller is to find a crisp control action from the fuzzy rule-base and from a set of crisp inputs. In this paper we modify the classical Fuzzy Inference Engine in order to activate a set of rules having the same conclusion; thus we obtain a fuzzy set as output (like as in Generalized Modus Ponens reasoning), which can be defuzzified in order to obtain a crisp value. Usually, the inference rules used in a fuzzy logic controller are given by a domain expert; in our system, these rules are automatically induced as fuzzy association rules starting from a training set. The fuzzy confidence value associated with each rule is used to obtain the fuzzy set inferred by our system.