Improved free-weighting matrix approach for stability analysis of discrete-time recurrent neural networks with time-varying delay

Min Wu, Fang Liu, Peng Shi, Yong He, Ryuichi Yokoyama

Research output: Contribution to journalArticle

46 Citations (Scopus)

Abstract

This paper deals with the problem of exponential stability for a class of discrete-time recurrent neural networks with time-varying delay by employing an improved free-weighting matrix approach. The relationship among the time-varying delay, its upper bound and their difference is taken into account. As a result, a new and less conservative delay-dependent stability criterion is obtained without ignoring any useful terms on the difference of a Lyapunov function, which is expressed in terms of linear matrix inequalities. Finally, numerical examples are given to demonstrate the effectiveness of the proposed techniques.

Original languageEnglish
Pages (from-to)690-694
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume55
Issue number7
DOIs
Publication statusPublished - 2008 Jul

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Keywords

  • Delay-dependent stability
  • Discrete-time recurrent neural networks
  • Linear matrix inequalities (LMIs)
  • Lyapunov function
  • Time-varying delay

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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