Global exponential periodicity of a class of recurrent neural networks with oscillating parameters and time-varying delays

Boshan Chen, Jun Wang

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)

Abstract

In this paper, we present the analytical results on the global exponential periodicity of a class of recurrent neural networks with oscillating parameters and time-varying delays. Sufficient conditions are derived for ascertaining the existence, uniqueness and global exponential periodicity of the oscillatory solution of such recurrent neural networks by using the comparison principle and mixed monotone operator method. The periodicity results extend or improve existing stability results for the class of recurrent neural networks with and without time delays.

Original languageEnglish
Pages (from-to)1440-1448
Number of pages9
JournalIEEE Transactions on Neural Networks
Volume16
Issue number6
DOIs
Publication statusPublished - Nov 2005
Externally publishedYes

Keywords

  • Global exponential periodicity
  • Global exponential stability
  • Mixed monotone operator
  • Neural networks
  • Oscillating connections
  • Periodic oscillation
  • Time-varying delay

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