Incorporating Prior Knowledge in Fuzzy -Regression Models – Application to System Identification

The identification of fuzzy c-regression models (FCRM) suffers from several problems characteristic of all calculusbased optimization methods, including good initialization, avoiding local minima and determining the number of clusters. This paper presents a grey-box approach that can solve the above-mentioned problems with the use of prior knowledge based constrained prototypes. The proposed approach has been applied to system identification, where the FCRM is used to initialize a Takagi-Sugeno fuzzy model of a nonlinear dynamic process. An example is shown to illustrate how knowledge about the type of nonlinearity can be incorporated into the FCRM used in the identification procedure.