Nonlinear-Systems Model Identification with AdditiveMultiplicative Fuzzy Neural Network
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Graphical Abstract
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Abstract
A model identification approach of nonlinear systems where only the input-output data of the identified system are available is presented. To automatically acquire the fuzzy rule-base and the initial parameters of the fuzzy model, an unsupervised clustering method is used in structure identification. Based on the cluster result, a Fuzzy Neural Network (FNN) is constructed to match with it. The FNN is trained by its learning algorithm to obtain a precise fuzzy model and realize parameter identification. Finally, the effectiveness of the proposed technique is confirmed by the simulation results of two nonlinear systems.
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