Global exponential stability of recurrent neural networks with time-dependent switching dynamics

Zhigang Zeng, Jun Wang, Tingwen Huang

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

Abstract

In this paper, the switching dynamics of recurrent neural networks are studied. Sufficient conditions on global exponential stability with an arbitrary switching law or a dwell time switching law and the estimates of Lyapunov exponent are obtained. The obtained results can be used to analyze and synthesize a family of continuous-time configurations with the switching between the configurations. Specially, the obtained results are new and efficacious for the switching between the stable and unstable configurations. Finally, simulation results are discussed to illustrate the theoretical results.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings
Pages583-592
Number of pages10
EditionPART 2
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event19th International Conference on Artificial Neural Networks, ICANN 2009 - Limassol, Cyprus
Duration: 14 Sep 200917 Sep 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5769 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Artificial Neural Networks, ICANN 2009
Country/TerritoryCyprus
CityLimassol
Period14/09/0917/09/09

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