Fuzzy Model Identification Based on Rough Set Data Analysis
Birkhäuser Boston eBooks · 2007
It is an open problem to model nonlinear systems with uncertainties. In Chapter 2, we developed an identification algorithm based on the Takagi-Sugeno fuzzy model. The fuzzy modeling procedure in Chapter 2 can be divided into three steps: premise structure identification, premise parameters identification, and consequent parameters identification. The premise structure identification procedure is done in two phases: (1) Identify the input structure, i.e., the significant input variables are identified among all possible input candidates; (2) assign fuzzy membership functions. In Chapter 2, we introduced an identification algorithm which included both phases in a uniform processes. We can also deal with them in two individual processes.