Global exponential stability of recurrent neural networks with time-varying delays in the presence of strong external stimuli

Zhigang Zeng, Jun Wang

Research output: Contribution to journalArticlepeer-review

104 Citations (Scopus)

Abstract

This paper presents new theoretical results on the global exponential stability of recurrent neural networks with bounded activation functions and bounded time-varying delays in the presence of strong external stimuli. It is shown that the Cohen-Grossberg neural network is globally exponentially stable, if the absolute value of the input vector exceeds a criterion. As special cases, the Hopfield neural network and the cellular neural network are examined in detail. In addition, it is shown that criteria herein, if partially satisfied, can still be used in combination with existing stability conditions. Simulation results are also discussed in two illustrative examples.

Original languageEnglish
Pages (from-to)1528-1537
Number of pages10
JournalNeural Networks
Volume19
Issue number10
DOIs
Publication statusPublished - Dec 2006
Externally publishedYes

Keywords

  • Exponential stability
  • Recurrent neural networks
  • Strong external stimuli
  • Time-varying

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