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《Journal of Zhejiang University(Engineering Science)》 2001-01
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Identification of nonlinear systems using robust stable recurrent high order neural networks

ZHU Yong, QIAN Ji-xing\;(Institute of Industrial Process Control, Zhejiang Uni., Hangzhou 310027, China)  
For identification of nonlinear systems using recurrent high order neural networks, a set of available robust stable learning rules and the corresponding structures based on the Lyapunov stability method are presented. It ensures that the identification error and parameters of the neural networks are stable in the sense of uniform ultimate boundedness when a nonlinear system is identified, even in the case there exists modeling error. The simulation shows that the proposed learning rules are effective.
【CateGory Index】: TP183
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1 XU Zhong min, LAN Yong chao, CHENG Guo dong (State Key Laboratory of Frozen Soil Engineering, CAREERI, CAS, Lanzhou Gansu 730000, China);A Study on Runoff Forecast by Aritifical Neural Network Model[J];Journal of Glaciolgy and Geocryology;2000-04
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