Skip to main navigation Skip to search Skip to main content

On Robust Exponential stability of a class of attractor neural networks

  • Hohai University
  • Southeast University, Nanjing
  • Nanjing Tech University

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

Abstract

Robust Exponential stability of continuous-time attractor neural networks with delays is discussed. A new sufficient condition ensuring existence and uniqueness of periodic solution for a general class of interval dynamical systems are obtained. Discrete-time analogue of the continuous-time systems with periodic input is formulated and we study their dynamical characteristics. The robust exponential stability and periodicity of the continuous-time systems is preserved by the discrete-time analogue without any restriction imposed on the uniform discretization step-size.

Original languageEnglish
Title of host publicationProceedings of the 16th IFAC World Congress, IFAC 2005
Pages94-99
Number of pages6
StatePublished - 2005
Externally publishedYes
Event16th Triennial World Congress of International Federation of Automatic Control, IFAC 2005 - Prague, Czech Republic
Duration: 3 Jul 20058 Jul 2005

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume16
ISSN (Print)1474-6670

Conference

Conference16th Triennial World Congress of International Federation of Automatic Control, IFAC 2005
Country/TerritoryCzech Republic
CityPrague
Period3/07/058/07/05

Keywords

  • Discrete-time analogue
  • Periodic solution
  • Robust
  • Stability

Fingerprint

Dive into the research topics of 'On Robust Exponential stability of a class of attractor neural networks'. Together they form a unique fingerprint.

Cite this