Global exponential stability and periodic solutions of recurrent neural networks with delays

He Huang, Jinde Cao, Jun Wang

Research output: Contribution to journalArticlepeer-review

147 Citations (Scopus)

Abstract

In this Letter, by utilizing the Lyapunov functional method, applying M-matrix and topological degree theory, we analyze the global exponential stability and the existence of periodic solutions of a class of recurrent neural networks with delays. Some simple and new sufficient conditions ensuring existence, uniqueness and global exponential stability of the equilibrium point and periodic solutions of delayed recurrent neural networks are obtained, which do not require the activation functions to be differentiable, bounded and monotone nondecreasing. In addition, two examples are also given to illustrate the theory.

Original languageEnglish
Pages (from-to)393-404
Number of pages12
JournalPhysics Letters, Section A: General, Atomic and Solid State Physics
Volume298
Issue number5-6
DOIs
Publication statusPublished - 17 Jun 2002
Externally publishedYes

Keywords

  • Delays
  • Global exponential stability
  • Lyapunov function
  • M-matrix
  • Periodic solutions
  • Recurrent neural networks
  • Topological degree

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