Eliciting transparent fuzzy model using differential evolution

In this paper a new technique for eliciting a fuzzy inference system (FIS) from data for nonlinear systems is proposed. The strategy is conducted in two phases: in the first one, subtractive clustering is applied to extract a set of fuzzy rules, in the second phase, the generated fuzzy rule base is refined and redundant rules are removed on the basis of an interpretability measure. Finally, centres and widths of the membership functions are tuned by means differential evolution. Case study is presented to illustrate the efficiency and accuracy of the proposed approach.