A delay-dependent approach to stability for static recurrent neural networks with mixed time-varying delays

Chienyu Lu, Tejen Su, Yihui Su, Shinchun Huang

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

This paper performs a globed stability analysis of a particular class of recurrent neural networks (RNN) in the static neural network models with both discrete and distributed time-varying delays. Both Lipschitz continuous activation function and monotone nondecreasing activation function are considered. Globally delay-dependent stability criteria are derived in the form of linear matrix inequalities (LMI) through the use of Leibniz-Newton formula and relaxation matrices. Moreover, the constraint that derivative of time-varying delays must be smaller than one is released for the proposed control scheme. Finally, two numerical examples are given to illustrate the effectiveness of the proposed criterion.

Original languageEnglish
Pages (from-to)1661-1672
Number of pages12
JournalInternational Journal of Innovative Computing, Information and Control
Volume4
Issue number7
Publication statusPublished - 2008 Jul 1

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All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Computational Theory and Mathematics

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