Improved delay-dependent stability analysis for uncertain stochastic neural networks with time-varying delay

Fang Liu, Min Wu, Yong He, Ryuichi Yokoyama

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper focuses on the problem of delay-dependent robust stability analysis for a class of uncertain stochastic neural networks with time-varying delay by employing improved free-weighting matrix method. Taking the relationship among the time-varying delay, its upper bound and their difference into account and using Itô's differential formula, some improved LMI-based delay-dependent stability criteria for stochastic neural networks are obtained without ignoring any terms, which guarantee systems globally robustly stochastically stable in the mean square. Finally, three numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method.

Original languageEnglish
Pages (from-to)441-449
Number of pages9
JournalNeural Computing and Applications
Volume20
Issue number3
DOIs
Publication statusPublished - 2011 Apr

Keywords

  • It̂'s differential formula
  • Linear matrix inequality (LMI)
  • Robust stability
  • Time-varying delay
  • Uncertain stochastic neural networks

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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