Absolute exponential stability of a class of continuous-time recurrent neural networks

Sanqing Hu, Jun Wang

Research output: Contribution to journalArticlepeer-review

59 Citations (Scopus)

Abstract

This paper presents a new result on absolute exponential stability (AEST) of a class of continuous-time recurrent neural networks with locally Lipschitz continuous and monotone nondecreasing activation functions. The additively diagonally stable connection weight matrices are proven to be able to guarantee AEST of the neural networks. The AEST result extends and improves the existing absolute stability and AEST ones in the literature.

Original languageEnglish
Pages (from-to)35-45
Number of pages11
JournalIEEE Transactions on Neural Networks
Volume14
Issue number1
DOIs
Publication statusPublished - Jan 2003
Externally publishedYes

Keywords

  • Absolute exponential stability (AEST)
  • Additive diagonal stability
  • Diagonal semistability
  • Global exponential stability
  • H-matrix
  • Neural networks

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