An LMI approach to global asymptotic stability of the delayed Cohen-Grossberg neural network via nonsmooth analysis

Wenwu Yu, Jinde Cao, Jun Wang

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)

Abstract

In this paper, a linear matrix inequality (LMI) to global asymptotic stability of the delayed Cohen-Grossberg neural network is investigated by means of nonsmooth analysis. Several new sufficient conditions are presented to ascertain the uniqueness of the equilibrium point and the global asymptotic stability of the neural network. It is noted that the results herein require neither the smoothness of the behaved function, or the activation function nor the boundedness of the activation function. In addition, from theoretical analysis, it is found that the condition for ensuring the global asymptotic stability of the neural network also implies the uniqueness of equilibrium. The obtained results improve many earlier ones and are easy to apply. Some simulation results are shown to substantiate the theoretical results.

Original languageEnglish
Pages (from-to)810-818
Number of pages9
JournalNeural Networks
Volume20
Issue number7
DOIs
Publication statusPublished - Sep 2007
Externally publishedYes

Keywords

  • Cohen-Grossberg neural networks
  • Global asymptotic stability
  • LMI technique
  • Lyapunov functional method
  • Nonsmooth analysis
  • Time delay

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