Delay-range-dependent global robust passivity analysis of discrete-time uncertain recurrent neural networks with interval time-varying delay

Chien Yu Lu, Chin Wen Liao, Hsun Heng Tsai

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5 Citations (Scopus)

Abstract

This paper examines a passivity 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. Based on an appropriate type of Lyapunov functional, sufficient passivity conditions for the DRNNs are derived in terms of a family of linear matrix inequalities (LMIs). Two numerical examples are given to illustrate the effectiveness and applicability.

Original languageEnglish
Article number430158
JournalDiscrete Dynamics in Nature and Society
Volume2009
DOIs
Publication statusPublished - 2009 Nov 24

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Interval Time-varying Delay
Passivity
Recurrent neural networks
Recurrent Neural Networks
Discrete-time
Time-varying Parameters
Dependent
Activation Function
Lyapunov Functional
Parameter Uncertainty
Linear matrix inequalities
Range of data
Lipschitz
Matrix Inequality
Linear Inequalities
Chemical activation
Sufficient
Norm
Numerical Examples
Class

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation

Cite this

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