A delay-dependent approach to design state estimator for discretestochastic recurrent neural network with interval time-varying delays

Chin Wen Liao, Chien Yu Lu, Kai Yuan Zheng, Chien Chung Ting

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

This paper deals with the problem of state estimation for discretestochastic recurrent neural network with interval time-delays. The activationfunctions are assumed to be globally Lipschitz continuous. Attention is focusedon the design of a state estimator which ensures the global stability of theestimation error dynamics. A delay-dependent condition with dependence on theupper and lower bounds of the delays is given in terms of a linear matrixinequality (LMI) to solve the neuron state estimation problem. When this LMI isfeasible, the expression of a desired state estimator is also presented. Inaddition, slack matrices are introduced to reduce the conservatism of thecondition. A numerical example is provided to demonstrate the applicability ofthe proposed approach. ICIC International

Original languageEnglish
Pages (from-to)465-470
Number of pages6
JournalICIC Express Letters
Volume3
Issue number3
Publication statusPublished - 2009 Sep 1

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Recurrent neural networks
State estimation
Neurons
Time delay

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Control and Systems Engineering

Cite this

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A delay-dependent approach to design state estimator for discretestochastic recurrent neural network with interval time-varying delays. / Liao, Chin Wen; Lu, Chien Yu; Zheng, Kai Yuan; Ting, Chien Chung.

In: ICIC Express Letters, Vol. 3, No. 3, 01.09.2009, p. 465-470.

Research output: Contribution to journalArticle

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