A delay-range-dependent approach to global robust stability for discrete-time uncertain recurrent neural networks with interval time-varying delay

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6 Citations (Scopus)

Abstract

The current paper performs a global robust stability analysis for a class of discrete-time recurrent neural networks (DRNNs) with norm-bounded time-varying parameter uncertainties and interval time-varying delay. The activation functions are assumed to be globally Lipschitz continuous. An appropriate type of Lyapunov functional is proposed to establish the sufficient conditions for the DRNNs. The criteria are formulated by means of the feasibility of linear matrix inequalities (LMIs), which can be easily checked in practice. Two numerical examples are given to illustrate the effectiveness and applicability.

Original languageEnglish
Pages (from-to)1123-1132
Number of pages10
JournalProceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering
Volume221
Issue number8
DOIs
Publication statusPublished - 2007 Dec 1

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Recurrent neural networks
Linear matrix inequalities
Chemical activation
Robust stability
Uncertainty

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

Cite this

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abstract = "The current paper performs a global robust stability analysis for a class of discrete-time recurrent neural networks (DRNNs) with norm-bounded time-varying parameter uncertainties and interval time-varying delay. The activation functions are assumed to be globally Lipschitz continuous. An appropriate type of Lyapunov functional is proposed to establish the sufficient conditions for the DRNNs. The criteria are formulated by means of the feasibility of linear matrix inequalities (LMIs), which can be easily checked in practice. Two numerical examples are given to illustrate the effectiveness and applicability.",
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