Some stability properties of recurrent neural networks

Jennie Si, Ching Fang Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Stability properties of recurrent neural networks are investigated using Lyapunov stability theory and functional analytic means. Sufficient conditions for the global asymptotic stability and exponentially asymptotic stability of equilibrium points of a class of recurrent neural networks are provided. The results obtained can be applied when recurrent neural networks are used as computation models, in particular, as optimization models. The results may also be used as stability analysis tools for some class of nonlinear control systems.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
PublisherAmerican Automatic Control Council
Pages3346-3350
Number of pages5
Volume3
StatePublished - 1994
EventProceedings of the 1994 American Control Conference. Part 1 (of 3) - Baltimore, MD, USA
Duration: Jun 29 1994Jul 1 1994

Other

OtherProceedings of the 1994 American Control Conference. Part 1 (of 3)
CityBaltimore, MD, USA
Period6/29/947/1/94

ASJC Scopus subject areas

  • Control and Systems Engineering

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