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Dynamics Analysis of a New Fractional-Order Hopfield Neural Network with Delay and Its Generalized Projective Synchronization

In this paper, a new three-dimensional fractional-order Hopfield-type neural network with delay is proposed. The system has a unique equilibrium point at the origin, which is a saddle point with index two, hence unstable. Intermittent chaos is found in this system. The complex dynamics are analyzed...

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Detalles Bibliográficos
Autores principales: Hu, Han-Ping, Wang, Jia-Kun, Xie, Fei-Long
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514113/
https://www.ncbi.nlm.nih.gov/pubmed/33266717
http://dx.doi.org/10.3390/e21010001
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author Hu, Han-Ping
Wang, Jia-Kun
Xie, Fei-Long
author_facet Hu, Han-Ping
Wang, Jia-Kun
Xie, Fei-Long
author_sort Hu, Han-Ping
collection PubMed
description In this paper, a new three-dimensional fractional-order Hopfield-type neural network with delay is proposed. The system has a unique equilibrium point at the origin, which is a saddle point with index two, hence unstable. Intermittent chaos is found in this system. The complex dynamics are analyzed both theoretically and numerically, including intermittent chaos, periodicity, and stability. Those phenomena are confirmed by phase portraits, bifurcation diagrams, and the Largest Lyapunov exponent. Furthermore, a synchronization method based on the state observer is proposed to synchronize a class of time-delayed fractional-order Hopfield-type neural networks.
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spelling pubmed-75141132020-11-09 Dynamics Analysis of a New Fractional-Order Hopfield Neural Network with Delay and Its Generalized Projective Synchronization Hu, Han-Ping Wang, Jia-Kun Xie, Fei-Long Entropy (Basel) Article In this paper, a new three-dimensional fractional-order Hopfield-type neural network with delay is proposed. The system has a unique equilibrium point at the origin, which is a saddle point with index two, hence unstable. Intermittent chaos is found in this system. The complex dynamics are analyzed both theoretically and numerically, including intermittent chaos, periodicity, and stability. Those phenomena are confirmed by phase portraits, bifurcation diagrams, and the Largest Lyapunov exponent. Furthermore, a synchronization method based on the state observer is proposed to synchronize a class of time-delayed fractional-order Hopfield-type neural networks. MDPI 2018-12-20 /pmc/articles/PMC7514113/ /pubmed/33266717 http://dx.doi.org/10.3390/e21010001 Text en © 2018 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
Hu, Han-Ping
Wang, Jia-Kun
Xie, Fei-Long
Dynamics Analysis of a New Fractional-Order Hopfield Neural Network with Delay and Its Generalized Projective Synchronization
title Dynamics Analysis of a New Fractional-Order Hopfield Neural Network with Delay and Its Generalized Projective Synchronization
title_full Dynamics Analysis of a New Fractional-Order Hopfield Neural Network with Delay and Its Generalized Projective Synchronization
title_fullStr Dynamics Analysis of a New Fractional-Order Hopfield Neural Network with Delay and Its Generalized Projective Synchronization
title_full_unstemmed Dynamics Analysis of a New Fractional-Order Hopfield Neural Network with Delay and Its Generalized Projective Synchronization
title_short Dynamics Analysis of a New Fractional-Order Hopfield Neural Network with Delay and Its Generalized Projective Synchronization
title_sort dynamics analysis of a new fractional-order hopfield neural network with delay and its generalized projective synchronization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514113/
https://www.ncbi.nlm.nih.gov/pubmed/33266717
http://dx.doi.org/10.3390/e21010001
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