Fuzzy modeling based on premise optimization

The task of fuzzy modeling involves specification of rule antecedents and determination of their consequent counterparts. Rule premises appear a critical issue because they correspond to the structure of a fuzzy model. The paper proposes an approach to extracting fuzzy rules from training examples by means of premise optimization. In order to construct a 'parsimonious' fuzzy model with high generalization ability, general premise structure allowing incomplete compositions of input variables as well as OR-connections of linguistic terms is considered. A genetic algorithm is utilized to optimize both premise structure of rules and fuzzy set membership functions at the same time. Determination of rule conclusions is nested in the premise learning, where consequences of individual rules are determined under fixed preconditions. The proposed method was applied to the well-known gas furnace data of Box and Jenkins to show its validity and to compare its performance with that of other works.