Global exponential stability and periodicity of recurrent neural networks with time delays

Jinde Cao, Jun Wang

Research output: Contribution to journalArticlepeer-review

303 Citations (Scopus)

Abstract

In this paper, the global exponential stability and periodicity of a class of recurrent neural networks with time delays are addressed by using Lyapunov functional method and inequality techniques. The delayed neural network includes the well-known Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks as its special cases. New criteria are found to ascertain the global exponential stability and periodicity of the recurrent neural networks with time delays, and are also shown to be different from and improve upon existing ones.

Original languageEnglish
Pages (from-to)920-931
Number of pages12
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Volume52
Issue number5
DOIs
Publication statusPublished - May 2005
Externally publishedYes

Keywords

  • Inequality
  • Lyapunov method
  • Periodicity
  • Recurrent neural networks
  • Stability
  • Time delay

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