Global Asymptotic Stability and Global Exponential Stability of Neural Networks With Unbounded Time-Varying Delays

Zhigang Zeng, Jun Wang, Xiaoxin Liao

Research output: Contribution to journalArticlepeer-review

111 Citations (Scopus)

Abstract

This brief studies the global asymptotic stability and the global exponential stability of neural networks with unbounded time-varying delays and with bounded and Lipschitz continuous activation functions. Several sufficient conditions for the global exponential stability and global asymptotic stability of such neural networks are derived. The new results given in the brief extend the existing relevant stability results in the literature to cover more general neural networks.

Original languageEnglish
Pages (from-to)168-173
Number of pages6
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume52
Issue number3
DOIs
Publication statusPublished - 5 Mar 2005
Externally publishedYes

Keywords

  • Global asymptotic stability
  • global exponential stability
  • neural networks
  • unbounded time-varying delay(UDNN)

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