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A New Model for Complex Dynamical Networks Considering Random Data Loss

Model construction is a very fundamental and important issue in the field of complex dynamical networks. With the state-coupling complex dynamical network model proposed, many kinds of complex dynamical network models were introduced by considering various practical situations. In this paper, aiming...

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Detalles Bibliográficos
Autores principales: Wu, Xu, Jiang, Guo-Ping, Wang, Xinwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515327/
https://www.ncbi.nlm.nih.gov/pubmed/33267510
http://dx.doi.org/10.3390/e21080797
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author Wu, Xu
Jiang, Guo-Ping
Wang, Xinwei
author_facet Wu, Xu
Jiang, Guo-Ping
Wang, Xinwei
author_sort Wu, Xu
collection PubMed
description Model construction is a very fundamental and important issue in the field of complex dynamical networks. With the state-coupling complex dynamical network model proposed, many kinds of complex dynamical network models were introduced by considering various practical situations. In this paper, aiming at the data loss which may take place in the communication between any pair of directly connected nodes in a complex dynamical network, we propose a new discrete-time complex dynamical network model by constructing an auxiliary observer and choosing the observer states to compensate for the lost states in the coupling term. By employing Lyapunov stability theory and stochastic analysis, a sufficient condition is derived to guarantee the compensation values finally equal to the lost values, namely, the influence of data loss is finally eliminated in the proposed model. Moreover, we generalize the modeling method to output-coupling complex dynamical networks. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed model.
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spelling pubmed-75153272020-11-09 A New Model for Complex Dynamical Networks Considering Random Data Loss Wu, Xu Jiang, Guo-Ping Wang, Xinwei Entropy (Basel) Article Model construction is a very fundamental and important issue in the field of complex dynamical networks. With the state-coupling complex dynamical network model proposed, many kinds of complex dynamical network models were introduced by considering various practical situations. In this paper, aiming at the data loss which may take place in the communication between any pair of directly connected nodes in a complex dynamical network, we propose a new discrete-time complex dynamical network model by constructing an auxiliary observer and choosing the observer states to compensate for the lost states in the coupling term. By employing Lyapunov stability theory and stochastic analysis, a sufficient condition is derived to guarantee the compensation values finally equal to the lost values, namely, the influence of data loss is finally eliminated in the proposed model. Moreover, we generalize the modeling method to output-coupling complex dynamical networks. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed model. MDPI 2019-08-15 /pmc/articles/PMC7515327/ /pubmed/33267510 http://dx.doi.org/10.3390/e21080797 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wu, Xu
Jiang, Guo-Ping
Wang, Xinwei
A New Model for Complex Dynamical Networks Considering Random Data Loss
title A New Model for Complex Dynamical Networks Considering Random Data Loss
title_full A New Model for Complex Dynamical Networks Considering Random Data Loss
title_fullStr A New Model for Complex Dynamical Networks Considering Random Data Loss
title_full_unstemmed A New Model for Complex Dynamical Networks Considering Random Data Loss
title_short A New Model for Complex Dynamical Networks Considering Random Data Loss
title_sort new model for complex dynamical networks considering random data loss
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515327/
https://www.ncbi.nlm.nih.gov/pubmed/33267510
http://dx.doi.org/10.3390/e21080797
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