Delay-dependent stability analysis for recurrent neural networks with time-varying delay

Chien-Yu Lu, T. J. Su, S. C. Huang

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

A global stability analysis of a particular class of recurrent neural networks with time-varying delay is performed. Both Lipschitz continuous and monotone non-decreasing activation functions are considered. Globally asymptotically delay-dependent stability criteria are derived in the form of linear matrix inequalities through the use of Leibniz-Newton formula and relaxation matrices. Finally, two numerical examples are given to illustrate the effectiveness of the given criterion.

Original languageEnglish
Pages (from-to)736-742
Number of pages7
JournalIET Control Theory and Applications
Volume2
Issue number8
DOIs
Publication statusPublished - 2008 Jul 24

Fingerprint

Delay-dependent Criteria
Delay-dependent Stability
Recurrent neural networks
Global Analysis
Activation Function
Stability criteria
Recurrent Neural Networks
Time-varying Delay
Global Stability
Linear matrix inequalities
Stability Criteria
Lipschitz
Matrix Inequality
Linear Inequalities
Stability Analysis
Monotone
Chemical activation
Numerical Examples
Class
Form

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Control and Optimization
  • Electrical and Electronic Engineering

Cite this

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Delay-dependent stability analysis for recurrent neural networks with time-varying delay. / Lu, Chien-Yu; Su, T. J.; Huang, S. C.

In: IET Control Theory and Applications, Vol. 2, No. 8, 24.07.2008, p. 736-742.

Research output: Contribution to journalArticle

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