Structural Identification of Fuzzy Model

The purpose of this paper is to present a mathematical tool to build a fuzzy model of a nonlinear system using its input-output data. The phase plane of system is divided into sub-regions and a linear model is assigned for each of these regions. This linear model is represented either in state-space form. To determine the pre-selected parameters of the linear system model under study, least-square identification method is used. Then these linear models are arranged in a fuzzy manner to characterize the overall system behavior. The proposed methodology is verified through simulation on a numeric example.