On-line nonlinear systems identification via dynamic neural networks with multi-time scales

  • Xuan Han*
  • , Wen Fang Xie
  • , Xue Mei Ren
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, an new on-line identification algorithm with dead-zone function is proposed for nonlinear systems identification via dynamic neural networks with different time-scales including the aspects of fast and slow phenomenon. The main contribution of the paper is that the Lyapunov function and singularly perturbed techniques are used to develop the on-line update laws for both dynamic neural networks weights and the linear part parameters of the neural network model. On example is also given to demonstrate the effectiveness of the proposed identification algorithm.

Original languageEnglish
Title of host publication2010 49th IEEE Conference on Decision and Control, CDC 2010
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4411-4416
Number of pages6
ISBN (Print)9781424477456
DOIs
Publication statusPublished - 2010
Event49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, United States
Duration: 15 Dec 201017 Dec 2010

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference49th IEEE Conference on Decision and Control, CDC 2010
Country/TerritoryUnited States
CityAtlanta
Period15/12/1017/12/10

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