Identification of Fuzzy Inference System Based on Information Granulation

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with "conventional" evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of...

• Type: Journal
• Year: 2010
• Where: ITIIS
• Authors: Wei Huang, Lixin Ding, Sung-Kwun Oh, Chang-Won Jeong, Su-Chong Joo
• Keywords: Fuzzy Inference, Fuzzy Models, Inference Systems, ITIIS 2010