An improved algebraic criterion for global exponential stability of recurrent neural networks with time-varying delays

Yi Shen, Jun Wang

Research output: Contribution to journalArticlepeer-review

112 Citations (Scopus)

Abstract

This brief paper presents an M-matrix-based algebraic criterion for the global exponential stability of a class of recurrent neural networks with decreasing time-varying delays. The criterion improves some previous criteria based on M-matrix and is easy to be verified with the connection weights of the recurrent neural networks with decreasing time-varying delays. In addition, the rate of exponential convergence can be estimated via a simple computation based on the criterion herein.

Original languageEnglish
Pages (from-to)528-531
Number of pages4
JournalIEEE Transactions on Neural Networks
Volume19
Issue number3
DOIs
Publication statusPublished - Mar 2008
Externally publishedYes

Keywords

  • Global exponential stability
  • M-matrix
  • Recurrent neural networks
  • Time-varying delays

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