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A dynamic neural network model for nonlinear system identification

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4 Author(s)
Chi-Hsu Wang ; Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan ; Pin-Cheng Chen ; Ping-Zong Lin ; Tsu-Tian Lee,

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 identified performance.

Published in:

Information Reuse & Integration, 2009. IRI '09. IEEE International Conference on

Date of Conference:

10-12 Aug. 2009

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