Periodicity on Neutral-Type Inertial Neural Networks Incorporating Multiple Delays

The classical Hopefield neural networks have obvious symmetry, thus the study related to its dynamic behaviors has been widely concerned. This research article is involved with the neutral-type inertial neural networks incorporating multiple delays. By making an appropriate Lyapunov functional, one...

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Autores principales:	Jian Zhang, Ancheng Chang, Gang Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	neutral-type inertial neural networks periodic solution exponential stability multiple delays Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/bfc8df7044f1400a9dd4ed946af501c0
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spelling	oai:doaj.org-article:bfc8df7044f1400a9dd4ed946af501c02021-11-25T19:07:51ZPeriodicity on Neutral-Type Inertial Neural Networks Incorporating Multiple Delays10.3390/sym131122312073-8994https://doaj.org/article/bfc8df7044f1400a9dd4ed946af501c02021-11-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2231https://doaj.org/toc/2073-8994The classical Hopefield neural networks have obvious symmetry, thus the study related to its dynamic behaviors has been widely concerned. This research article is involved with the neutral-type inertial neural networks incorporating multiple delays. By making an appropriate Lyapunov functional, one novel sufficient stability criterion for the existence and global exponential stability of <i>T</i>-periodic solutions on the proposed system is obtained. In addition, an instructive numerical example is arranged to support the present approach. The obtained results broaden the application range of neutral-types inertial neural networks.Jian ZhangAncheng ChangGang YangMDPI AGarticleneutral-type inertial neural networksperiodic solutionexponential stabilitymultiple delaysMathematicsQA1-939ENSymmetry, Vol 13, Iss 2231, p 2231 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	neutral-type inertial neural networks periodic solution exponential stability multiple delays Mathematics QA1-939
spellingShingle	neutral-type inertial neural networks periodic solution exponential stability multiple delays Mathematics QA1-939 Jian Zhang Ancheng Chang Gang Yang Periodicity on Neutral-Type Inertial Neural Networks Incorporating Multiple Delays
description	The classical Hopefield neural networks have obvious symmetry, thus the study related to its dynamic behaviors has been widely concerned. This research article is involved with the neutral-type inertial neural networks incorporating multiple delays. By making an appropriate Lyapunov functional, one novel sufficient stability criterion for the existence and global exponential stability of <i>T</i>-periodic solutions on the proposed system is obtained. In addition, an instructive numerical example is arranged to support the present approach. The obtained results broaden the application range of neutral-types inertial neural networks.
format	article
author	Jian Zhang Ancheng Chang Gang Yang
author_facet	Jian Zhang Ancheng Chang Gang Yang
author_sort	Jian Zhang
title	Periodicity on Neutral-Type Inertial Neural Networks Incorporating Multiple Delays
title_short	Periodicity on Neutral-Type Inertial Neural Networks Incorporating Multiple Delays
title_full	Periodicity on Neutral-Type Inertial Neural Networks Incorporating Multiple Delays
title_fullStr	Periodicity on Neutral-Type Inertial Neural Networks Incorporating Multiple Delays
title_full_unstemmed	Periodicity on Neutral-Type Inertial Neural Networks Incorporating Multiple Delays
title_sort	periodicity on neutral-type inertial neural networks incorporating multiple delays
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bfc8df7044f1400a9dd4ed946af501c0
work_keys_str_mv	AT jianzhang periodicityonneutraltypeinertialneuralnetworksincorporatingmultipledelays AT anchengchang periodicityonneutraltypeinertialneuralnetworksincorporatingmultipledelays AT gangyang periodicityonneutraltypeinertialneuralnetworksincorporatingmultipledelays
_version_	1718410302691737600

Periodicity on Neutral-Type Inertial Neural Networks Incorporating Multiple Delays

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