Global exponential convergence of Cohen-Grossberg neural networks with time delays

Hongtao Lu, Ruiming Shen, Fu Lai Korris Chung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

40 Citations (Scopus)

Abstract

In this paper, we derive a general sufficient condition ensuring global exponential convergence of Cohen-Grossberg neural networks with time delays by constructing a novel Lyapunov functional and smartly estimating its derivative. The proposed condition is related to the convex combinations of the column-sum and the row-sum of the connection matrices and also relaxes the constraints on the network coefficients. Therefore, the proposed condition generalizes some previous results in the literature.
Original languageEnglish
Pages (from-to)1694-1696
Number of pages3
JournalIEEE Transactions on Neural Networks
Volume16
Issue number6
DOIs
Publication statusPublished - 1 Nov 2005

Keywords

  • Cohen-Grossberg neural networks
  • Global exponential stability
  • Time delay

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Hardware and Architecture

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