Synchronization Control for Stochastic Neural Networks with Mixed Time-Varying Delays

Synchronization control of stochastic neural networks with time-varying discrete and continuous delays has been investigated. A novel control scheme is proposed using the Lyapunov functional method and linear matrix inequality (LMI) approach. Sufficient conditions have been derived to ensure the glo...

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Detalles Bibliográficos
Autores principales: Zhu, Qing, Song, Aiguo, Fei, Shumin, Yang, Yuequan, Cao, Zhiqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4106077/
https://www.ncbi.nlm.nih.gov/pubmed/25110747
http://dx.doi.org/10.1155/2014/840185
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author Zhu, Qing
Song, Aiguo
Fei, Shumin
Yang, Yuequan
Cao, Zhiqiang
author_facet Zhu, Qing
Song, Aiguo
Fei, Shumin
Yang, Yuequan
Cao, Zhiqiang
author_sort Zhu, Qing
collection PubMed
description Synchronization control of stochastic neural networks with time-varying discrete and continuous delays has been investigated. A novel control scheme is proposed using the Lyapunov functional method and linear matrix inequality (LMI) approach. Sufficient conditions have been derived to ensure the global asymptotical mean-square stability for the error system, and thus the drive system synchronizes with the response system. Also, the control gain matrix can be obtained. With these effective methods, synchronization can be achieved. Simulation results are presented to show the effectiveness of the theoretical results.
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spelling pubmed-41060772014-08-10 Synchronization Control for Stochastic Neural Networks with Mixed Time-Varying Delays Zhu, Qing Song, Aiguo Fei, Shumin Yang, Yuequan Cao, Zhiqiang ScientificWorldJournal Research Article Synchronization control of stochastic neural networks with time-varying discrete and continuous delays has been investigated. A novel control scheme is proposed using the Lyapunov functional method and linear matrix inequality (LMI) approach. Sufficient conditions have been derived to ensure the global asymptotical mean-square stability for the error system, and thus the drive system synchronizes with the response system. Also, the control gain matrix can be obtained. With these effective methods, synchronization can be achieved. Simulation results are presented to show the effectiveness of the theoretical results. Hindawi Publishing Corporation 2014 2014-07-02 /pmc/articles/PMC4106077/ /pubmed/25110747 http://dx.doi.org/10.1155/2014/840185 Text en Copyright © 2014 Qing Zhu et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhu, Qing
Song, Aiguo
Fei, Shumin
Yang, Yuequan
Cao, Zhiqiang
Synchronization Control for Stochastic Neural Networks with Mixed Time-Varying Delays
title Synchronization Control for Stochastic Neural Networks with Mixed Time-Varying Delays
title_full Synchronization Control for Stochastic Neural Networks with Mixed Time-Varying Delays
title_fullStr Synchronization Control for Stochastic Neural Networks with Mixed Time-Varying Delays
title_full_unstemmed Synchronization Control for Stochastic Neural Networks with Mixed Time-Varying Delays
title_short Synchronization Control for Stochastic Neural Networks with Mixed Time-Varying Delays
title_sort synchronization control for stochastic neural networks with mixed time-varying delays
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4106077/
https://www.ncbi.nlm.nih.gov/pubmed/25110747
http://dx.doi.org/10.1155/2014/840185
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AT yangyuequan synchronizationcontrolforstochasticneuralnetworkswithmixedtimevaryingdelays
AT caozhiqiang synchronizationcontrolforstochasticneuralnetworkswithmixedtimevaryingdelays

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