Synchronization criteria for coupled stochastic neural networks with time-varying delays and leakage delay

M. J. Park; O. M. Kwon; Ju H. Park; S. M. Lee; E. J. Cha

doi:10.1016/j.jfranklin.2012.02.002

Synchronization criteria for coupled stochastic neural networks with time-varying delays and leakage delay

M. J. Park, O. M. Kwon, Ju H. Park, S. M. Lee, E. J. Cha

School of Electronics Engineering

Research output: Contribution to journal › Article › peer-review

76 Scopus citations

Abstract

This paper proposes new delay-dependent synchronization criteria for coupled stochastic neural networks with time-varying delays and leakage delay. By constructing a suitable Lyapunov-Krasovskiis functional and utilizing Finslers lemma, novel synchronization criteria for the networks are established in terms of linear matrix inequalities (LMIs) which can be easily solved by using the LMI toolbox in MATLAB. Three numerical examples are given to illustrate the effectiveness of the proposed methods.

Original language	English
Pages (from-to)	1699-1720
Number of pages	22
Journal	Journal of the Franklin Institute
Volume	349
Issue number	5
DOIs	https://doi.org/10.1016/j.jfranklin.2012.02.002
State	Published - Jun 2012

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10.1016/j.jfranklin.2012.02.002

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title = "Synchronization criteria for coupled stochastic neural networks with time-varying delays and leakage delay",

abstract = "This paper proposes new delay-dependent synchronization criteria for coupled stochastic neural networks with time-varying delays and leakage delay. By constructing a suitable Lyapunov-Krasovskiis functional and utilizing Finslers lemma, novel synchronization criteria for the networks are established in terms of linear matrix inequalities (LMIs) which can be easily solved by using the LMI toolbox in MATLAB. Three numerical examples are given to illustrate the effectiveness of the proposed methods.",

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AU - Lee, S. M.

AU - Cha, E. J.

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AB - This paper proposes new delay-dependent synchronization criteria for coupled stochastic neural networks with time-varying delays and leakage delay. By constructing a suitable Lyapunov-Krasovskiis functional and utilizing Finslers lemma, novel synchronization criteria for the networks are established in terms of linear matrix inequalities (LMIs) which can be easily solved by using the LMI toolbox in MATLAB. Three numerical examples are given to illustrate the effectiveness of the proposed methods.

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Synchronization criteria for coupled stochastic neural networks with time-varying delays and leakage delay

Abstract

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