Deriving sufficient conditions for global asymptotic stability of delayed neural networks via nonsmooth analysis - II

Houduo Qi, Liqun Qi, Xiaoqi Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

18 Citations (Scopus)

Abstract

Following our recent approach of nonsmooth analysis, we report a new set of sufficient conditions and its implications for the global asymptotic stability of delayed cellular neural networks (DCNN). The new conditions not only unify a string of previous stability results, but also yield strict improvement over them by allowing the symmetric part of the feedback matrix positive definite, hence enlarging the application domain of DCNNs. Advantages of the new results over existing ones are illustrated with examples. We also compare our results with those related results obtained via LMI approach.
Original languageEnglish
Pages (from-to)1701-1706
Number of pages6
JournalIEEE Transactions on Neural Networks
Volume16
Issue number6
DOIs
Publication statusPublished - 1 Nov 2005

Keywords

  • Equilibrium point
  • Global asymptotic stability
  • Lipschitzian functions
  • Neural networks
  • Nonsmooth analysis

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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