Fuzzy system modeling by fuzzy partition and GA hybrid schemes

This paper presents an approach to building multi-input and single-output fuzzy models. Such a model is composed of fuzzy implications, and its output is inferred by simplified reasoning. The implications are automatically generated by the structure and parameter identification. In structure identification, the optimal or near optimal number of fuzzy implications is determined in view of valid partition of data set. The parameters defining the fuzzy implications are identified by a GA (Genetic Algorithm) hybrid scheme to minimize mean square errors globally. Numerical examples are provided to evaluate the feasibility of the proposed approach. Comparison shows that the suggested approach can produce a fuzzy model with higher accuracy and a smaller number of fuzzy implications than the ones achieved previously in other methods. The proposed approach has also been applied to construct a fuzzy model for the navigation control of a mobile robot. The validity of the resultant model is demonstrated by experimentation.