Identifying fuzzy models utilizing genetic programming

Fuzzy models offer a convenient way to describe complex nonlinear systems. Moreover, they permit the user to deal with uncertainty and vagueness. Due to these advantages fuzzy models are employed in various fields of applicat ions, e.g. control, forecasting, and pattern recognition. Nevertheless, it has to be emphasized that the identification of a fuzzy model is a complex optimization task with many local minima. Genetic programming provides a wa y to solve such complex optimization problems. In this work, the use of gen etic programming to identify the input variables, the rule base and the inv olved membership functions of a fuzzy model is proposed. For this purpose, several new reproduction operators are introduced. (C) 2000 Elsevier Scienc e B.V. All rights reserved.