Fuzzy Model Identification

Abstract

Abstract Fuzzy model identification is an effective tool for the approx- imation of uncertain nonlinear systems on the basis of measured data. The identification of a fuzzy model using input-output data can be divided into two tasks: structure identification, which determines the type and number of the rules and membership functions, and parameter identification. For both structural and parametric adjustment, prior knowledge plays an im- portant role. Hence, in this book the rules of the fuzzy system are designed based on the available a priori knowledge and the parameters of the mem- bership, and the consequent functions are adapted in a learning process based on the available input-output data. Hence, this chapter is devoted mainly to the parameter identification of the proposed fuzzy models, but certain structure identification tools are also discussed.