Knowledge extraction from hierarchical fuzzy model obtained by fuzzy neural networks and genetic algorithm

Knowledge extraction from trained artificial neural networks has been studied by many researchers. Modeling of nonlinear systems using fuzzy neural networks (FNNs) is a promising approach to the knowledge acquisition, and a FNN is specially designed for knowledge extraction. The authors have proposed a hierarchical fuzzy modeling method using FNNs and a genetic algorithm (GA). This method can identify fuzzy models of nonlinear objects with strong nonlinearity. The disadvantage of the method is that the training of the FNN is time consuming. This paper presents a quick method for a rough search for proper structures in the antecedent of fuzzy models. The fine tuning of the acquired rough model is done by the FNN. This modeling method is quite efficient to identify precise fuzzy models of systems with strong nonlinearities. A simulation is done to show the effectiveness of the proposed method.