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Non-fragile mixed H(∞) and passive synchronization of Markov jump neural networks with mixed time-varying delays and randomly occurring controller gain fluctuation

This paper studies the non-fragile mixed H(∞) and passive synchronization problem for Markov jump neural networks. The randomly occurring controller gain fluctuation phenomenon is investigated for non-fragile strategy. Moreover, the mixed time-varying delays composed of discrete and distributed dela...

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Detalles Bibliográficos
Autor principal: Ma, Chao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391947/
https://www.ncbi.nlm.nih.gov/pubmed/28410394
http://dx.doi.org/10.1371/journal.pone.0175676
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author Ma, Chao
author_facet Ma, Chao
author_sort Ma, Chao
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description This paper studies the non-fragile mixed H(∞) and passive synchronization problem for Markov jump neural networks. The randomly occurring controller gain fluctuation phenomenon is investigated for non-fragile strategy. Moreover, the mixed time-varying delays composed of discrete and distributed delays are considered. By employing stochastic stability theory, synchronization criteria are developed for the Markov jump neural networks. On the basis of the derived criteria, the non-fragile synchronization controller is designed. Finally, an illustrative example is presented to demonstrate the validity of the control approach.
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spelling pubmed-53919472017-05-03 Non-fragile mixed H(∞) and passive synchronization of Markov jump neural networks with mixed time-varying delays and randomly occurring controller gain fluctuation Ma, Chao PLoS One Research Article This paper studies the non-fragile mixed H(∞) and passive synchronization problem for Markov jump neural networks. The randomly occurring controller gain fluctuation phenomenon is investigated for non-fragile strategy. Moreover, the mixed time-varying delays composed of discrete and distributed delays are considered. By employing stochastic stability theory, synchronization criteria are developed for the Markov jump neural networks. On the basis of the derived criteria, the non-fragile synchronization controller is designed. Finally, an illustrative example is presented to demonstrate the validity of the control approach. Public Library of Science 2017-04-14 /pmc/articles/PMC5391947/ /pubmed/28410394 http://dx.doi.org/10.1371/journal.pone.0175676 Text en © 2017 Chao Ma http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ma, Chao
Non-fragile mixed H(∞) and passive synchronization of Markov jump neural networks with mixed time-varying delays and randomly occurring controller gain fluctuation
title Non-fragile mixed H(∞) and passive synchronization of Markov jump neural networks with mixed time-varying delays and randomly occurring controller gain fluctuation
title_full Non-fragile mixed H(∞) and passive synchronization of Markov jump neural networks with mixed time-varying delays and randomly occurring controller gain fluctuation
title_fullStr Non-fragile mixed H(∞) and passive synchronization of Markov jump neural networks with mixed time-varying delays and randomly occurring controller gain fluctuation
title_full_unstemmed Non-fragile mixed H(∞) and passive synchronization of Markov jump neural networks with mixed time-varying delays and randomly occurring controller gain fluctuation
title_short Non-fragile mixed H(∞) and passive synchronization of Markov jump neural networks with mixed time-varying delays and randomly occurring controller gain fluctuation
title_sort non-fragile mixed h(∞) and passive synchronization of markov jump neural networks with mixed time-varying delays and randomly occurring controller gain fluctuation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391947/
https://www.ncbi.nlm.nih.gov/pubmed/28410394
http://dx.doi.org/10.1371/journal.pone.0175676
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