Uneven allocation of membership functions for hierarchical fuzzy modeling using genetic algorithm

Fuzzy modeling is a promising technique to describe input-output relationships of nonlinear system. This paper presents a new hierarchical fuzzy modeling method using Genetic Algorithm (GA). Uneven allocation of membership functions in the antecedent of each sub-model in the hierarchical fuzzy model can be achieved with the proposed method. This paper introduces a simple coding method and a quick rule identification method for efficient search for a sub-model using a Fuzzy Neural Network (FNN). The obtained hierarchical fuzzy model are probable to be more concise and more precise than those identified with the conventional methods.