Identification of Fuzzy Systems with the Aid of Genetic Fuzzy Granulation

Abstract

We propose the identification of fuzzy systems with the aid of genetic fuzzy granulation to carry out the model identification of complex and nonlinear systems. The proposed fuzzy model implements system structure and parameter identification with the aid of genetic algorithms and information granulation. To identify the structure of fuzzy rules we use genetic algorithms. Granulation of information realized with Hard C-Means clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method. An example is given to evaluate the validity of the proposed model.