Identification Method Research Of Fuzzy Neural Network For Nonlinear Systems

Nonlinear phenomena are general problems in every field of engineering technology, science research, natural world and human society activities. Nonlinear system identification is a hotspot that many savants are researching. The combination of the fuzzy logic and artificial neural network is the fuzzy neural system. It has the important theory meaning and the actual application value to use it in the research of complicated non-linear system. It has been widely used in the automatic controlled field.This paper mainly focused on the problem of fuzzy neural network identification of non-linearsystem .In this paper ,we propose a new neuro-fuzzy systems with Laplace membership function,andproved its universial approximation property ,And we get excellent modeling results fornonlinear systems by applying the new neuro-fuzzy model.In this paper, we also propose a new kind of optimal selection cluster algorithm,and thealtorithm can synchronously solve the identification problems If the new fuzzy neural networkmodel's structure and parameters.The higher identification precision is gained by apply the newfuzzy neural network model.The effect of simulating nonlinear systems shows validity of the scheme in this paper.