A dynamic neural network model for nonlinear system identification

Chi-Hsu Wang; Pin Cheng Chen; Ping Zong Lin; Tsu Tian Lee

doi:10.1109/IRI.2009.5211647

A dynamic neural network model for nonlinear system identification

Chi-Hsu Wang^*, Pin Cheng Chen, Ping Zong Lin, Tsu Tian Lee

^*Corresponding author for this work

Department of Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2009 IEEE International Conference on Information Reuse and Integration, IRI 2009
Pages	440-441
Number of pages	2
DOIs	https://doi.org/10.1109/IRI.2009.5211647
State	Published - 17 Nov 2009
Event	2009 IEEE International Conference on Information Reuse and Integration, IRI 2009 - Las Vegas, NV, United States Duration: 10 Aug 2009 → 12 Aug 2009

Publication series

Name	2009 IEEE International Conference on Information Reuse and Integration, IRI 2009

Conference

Conference	2009 IEEE International Conference on Information Reuse and Integration, IRI 2009
Country	United States
City	Las Vegas, NV
Period	10/08/09 → 12/08/09

Keywords

Dynamic neural network
Hopfield neural network
Lyapunov criterion
System identification

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10.1109/IRI.2009.5211647

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Cite this

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title = "A dynamic neural network model for nonlinear system identification",

abstract = "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.",

keywords = "Dynamic neural network, Hopfield neural network, Lyapunov criterion, System identification",

author = "Chi-Hsu Wang and Chen, {Pin Cheng} and Lin, {Ping Zong} and Lee, {Tsu Tian}",

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Wang, C-H, Chen, PC, Lin, PZ & Lee, TT 2009, A dynamic neural network model for nonlinear system identification. in 2009 IEEE International Conference on Information Reuse and Integration, IRI 2009., 5211647, 2009 IEEE International Conference on Information Reuse and Integration, IRI 2009, pp. 440-441, 2009 IEEE International Conference on Information Reuse and Integration, IRI 2009, Las Vegas, NV, United States, 10/08/09. https://doi.org/10.1109/IRI.2009.5211647

A dynamic neural network model for nonlinear system identification. / Wang, Chi-Hsu; Chen, Pin Cheng; Lin, Ping Zong; Lee, Tsu Tian.

2009 IEEE International Conference on Information Reuse and Integration, IRI 2009. 2009. p. 440-441 5211647 (2009 IEEE International Conference on Information Reuse and Integration, IRI 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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