A delay-dependent approach to robust stability for uncertain stochastic neural networks with time-varying delay

Chien-Yu Lu, Chin-Wen Liao, Koan Yuh Chang, Wen Jer Chang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper investigates the global delay-dependent robust stability in the mean square for uncertain stochastic neural networks with time-varying delay. The activation functions are assumed to be globally Lipschitz continuous. Based on a linear matrix inequality approach, globally delay-dependent robust stability criterion is derived by introducing some relaxation matrices which, when chosen properly, lead to a less conservative result. Two numerical examples are given to illustrate the effectiveness of the method.

Original languageEnglish
Pages (from-to)77-83
Number of pages7
JournalJournal of Marine Science and Technology
Volume18
Issue number1
Publication statusPublished - 2010 Feb 1

Fingerprint

Neural networks
matrix
Stability criteria
Linear matrix inequalities
Chemical activation
Robust stability
method

All Science Journal Classification (ASJC) codes

  • Oceanography
  • Ocean Engineering
  • Mechanics of Materials
  • Mechanical Engineering

Cite this

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abstract = "This paper investigates the global delay-dependent robust stability in the mean square for uncertain stochastic neural networks with time-varying delay. The activation functions are assumed to be globally Lipschitz continuous. Based on a linear matrix inequality approach, globally delay-dependent robust stability criterion is derived by introducing some relaxation matrices which, when chosen properly, lead to a less conservative result. Two numerical examples are given to illustrate the effectiveness of the method.",
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A delay-dependent approach to robust stability for uncertain stochastic neural networks with time-varying delay. / Lu, Chien-Yu; Liao, Chin-Wen; Chang, Koan Yuh; Chang, Wen Jer.

In: Journal of Marine Science and Technology, Vol. 18, No. 1, 01.02.2010, p. 77-83.

Research output: Contribution to journalArticle

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