Design of Information Granules-Based Fuzzy Systems Using Clustering Algorithm and Genetic Optimization

摘要

We introduce information granulation-based fuzzy systems 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 (GAs) and information granulation (IG). The design methodology emerges as a hybrid structural optimization and parametric optimization. IG realized with Hard C-Means (HCM) clustering help determine the initial parameters of fuzzy. And the initial parameters are tuned effectively with the aid of the GAs and the least square method (LSM). And we use GAs to identify the structure of fuzzy rules.