Improved global robust asymptotic stability criteria for delayed cellular neural networks

Shengyuan Xu, James Lam, Daniel W.C. Ho, Yun Zou

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

81 Citations (Scopus)

Abstract

This paper considers the problem of global robust stability analysis of delayed cellular neural networks (DCNNs) with norm-bounded parameter uncertainties. In terms of a linear matrix inequality, a new sufficient condition ensuring a nominal DCNN to have a unique equilibrium point which is globally asymptotically stable is proposed. This condition is shown to be a generalization and improvement over some previous criteria. Based on the stability result, a robust stability condition is developed, which contains an existing robust stability result as a special case. An example is provided to demonstrate the reduced conservativeness of the proposed results. © 2005 IEEE.
Original languageEnglish
Pages (from-to)1317-1321
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume35
Issue number6
DOIs
Publication statusPublished - Dec 2005

Research Keywords

  • Cellular neural network
  • Global asymptotic stability
  • Linear matrix inequality
  • Parameter uncertainty
  • Robust stability
  • Time delays

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