A systematic method for analyzing robust stability of interval neural networks with time-delays based on stability criteria

Zhenyuan Guo, Jun Wang, Zheng Yan

Research output: Contribution to journalArticlepeer-review

21 Citations (SciVal)

Abstract

This paper presents a systematic method for analyzing the robust stability of a class of interval neural networks with uncertain parameters and time delays. The neural networks are affected by uncertain parameters whose values are time-invariant and unknown, but bounded in given compact sets. Several new sufficient conditions for the global asymptotic/exponential robust stability of the interval delayed neural networks are derived. The results can be casted as linear matrix inequalities (LMIs), which are shown to be generalizations of some existing conditions. Compared with most existing results, the presented conditions are less conservative and easier to check. Two illustrative numerical examples are given to substantiate the effectiveness and applicability of the proposed robust stability analysis method.

Original languageEnglish
Pages (from-to)112-122
Number of pages11
JournalNeural Networks
Volume54
DOIs
Publication statusPublished - Jun 2014
Externally publishedYes

Keywords

  • Interval neural network
  • LMI
  • Robust stability
  • Time delay

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