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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publication2009 IEEE International Conference on Information Reuse and Integration, IRI 2009
Pages440-441
Number of pages2
DOIs
StatePublished - 17 Nov 2009
Event2009 IEEE International Conference on Information Reuse and Integration, IRI 2009 - Las Vegas, NV, United States
Duration: 10 Aug 200912 Aug 2009

Publication series

Name2009 IEEE International Conference on Information Reuse and Integration, IRI 2009

Conference

Conference2009 IEEE International Conference on Information Reuse and Integration, IRI 2009
CountryUnited States
CityLas Vegas, NV
Period10/08/0912/08/09

Keywords

  • Dynamic neural network
  • Hopfield neural network
  • Lyapunov criterion
  • System identification

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