Global Exponential Stability of a General Class of Recurrent Neural Networks with Time-Varying Delays

Zhigang Zeng, Jun Wang, Xiaoxin Liao

Research output: Contribution to journalArticlepeer-review

263 Citations (Scopus)

Abstract

This brief presents new theoretical results on the global exponential stability of neural networks with time-varying delays and Lipschitz continuous activation functions. These results include several sufficient conditions for the global exponential stability of general neural networks with time-varying delays and without monotone, bounded, or continuously differentiable activation function. In addition to providing new criteria for neural networks with time-varying delays, these stability conditions also improve upon the existing ones with constant time delays and without time delays. Furthermore, it is convenient to estimate the exponential convergence rates of the neural networks by using the results.

Original languageEnglish
Pages (from-to)1353-1358
Number of pages6
JournalIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Volume50
Issue number10
DOIs
Publication statusPublished - Oct 2003
Externally publishedYes

Keywords

  • Global exponential stability
  • Neural networks
  • Rate of exponential convergence
  • Time-varying delays

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