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.
Weitere Kapitel dieses Buchs durch Wischen aufrufen
- Fuzzy Model Identification
- Birkhäuser Boston
- Chapter 4