A hierarchical fuzzy modeling method using genetic algorithm for identification of concise submodels

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 submodel 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 submodel using a fuzzy neural network (FNN). The obtained hierarchical fuzzy model are more concise than those identified with the conventional methods.