A Dynamic Neural Network Model for Nonlinear System Identification

被引：0

作者：

Wang, Chi-Hsu ^{[1
]}

Chen, Pin-Cheng ^{[2
]}

Lin, Ping-Zong ^{[1
]}

Lee, Tsu-Tian ^{[3
]}

机构：

[1] Natl Chiao Tung Univ, Dept Elect & Control Engn, Hsinchu, Taiwan

[2] Natl Taipei Univ Technol, Dept Elect Engn, Taipei, Taiwan

[3] Natl Taipei Univ Technol, Taipei, Taiwan

来源：

PROCEEDINGS OF THE 2009 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION | 2008年

关键词：

system identification; dynamic neural network; Hopfield neural network; Lyapunov criterion;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a new dynamic neural network based on the Hopfield neural network is proposed to perform the nonlinear system identification. Convergent analysis is performed by the Lyapunov-like criterion to guarantee the error convergence during identification. Simulation results demonstrate that the proposed dynamic neural network trained by the Lyapunov approach can obtain good identifted performance.

引用

页码：440 / +

页数：2

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