Automated fuzzy neural networks for nonlinear system identification

This paper discusses the identification of nonlinear dynamic system using fuzzy neural networks. It focuses on both the structure uncertainty and the parameter uncertainty which have been widely explored in the literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated fuzzy neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance. Firstly, an automated support vector machine is proposed within a fixed time interval for a given network construction criterion. Then the network parameter updating algorithm is proposed with guaranteed bounded identification error. To cope structure uncertainty, a hysteresis strategy is proposed to enable fuzzy neural identifier switching with guaranteed network performance along the switching process. Both theoretic analysis and simulation example show the efficacy of the proposed method.