Transparent fuzzy modeling using fuzzy clustering and GAs

A combined approach to data-driven fuzzy rule-based modeling is described. The rules of an initial model are derived from data by means of a supervised clustering method that to a certain degree ensures the transparency of the resulting rule base. This model is, however suboptimal and a real-coded genetic algorithm (GA) is proposed to optimize simultaneously both the antecedent and the consequent variables. The GA is subjected to constraints concerning the semantic properties of the rule base, inherited from the initial model. Two modeling problems illustrate the power of the combined approach.