Anti-periodicity on high-order inertial Hopfield neural networks involving mixed delays

Abstract This paper deals with a class of high-order inertial Hopfield neural networks involving mixed delays. Utilizing differential inequality techniques and the Lyapunov function method, we obtain a sufficient assertion to ensure the existence and global exponential stability of anti-periodic sol...

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Main Authors:	Luogen Yao, Qian Cao
Format:	Article
Language:	English
Published:	SpringerOpen 2020-07-01
Series:	Journal of Inequalities and Applications
Subjects:	High-order inertial neural networks Anti-periodic solution Global exponential stability Mixed delay
Online Access:	http://link.springer.com/article/10.1186/s13660-020-02444-3

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spelling	doaj-bba050ddd7274002b9892684e2cb57722020-11-25T03:25:20ZengSpringerOpenJournal of Inequalities and Applications1029-242X2020-07-012020112210.1186/s13660-020-02444-3Anti-periodicity on high-order inertial Hopfield neural networks involving mixed delaysLuogen Yao0Qian Cao1Key Laboratory of Hunan Province for Statistical Learning and Intelligent Computation; School of Mathematics and Statistics, Hunan University of Technology and BusinessCollege of Mathematics and Physics, Hunan University of Arts and ScienceAbstract This paper deals with a class of high-order inertial Hopfield neural networks involving mixed delays. Utilizing differential inequality techniques and the Lyapunov function method, we obtain a sufficient assertion to ensure the existence and global exponential stability of anti-periodic solutions of the proposed networks. Moreover, an example with a numerical simulation is furnished to illustrate the effectiveness and feasibility of the theoretical results.http://link.springer.com/article/10.1186/s13660-020-02444-3High-order inertial neural networksAnti-periodic solutionGlobal exponential stabilityMixed delay
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Luogen Yao Qian Cao
spellingShingle	Luogen Yao Qian Cao Anti-periodicity on high-order inertial Hopfield neural networks involving mixed delays Journal of Inequalities and Applications High-order inertial neural networks Anti-periodic solution Global exponential stability Mixed delay
author_facet	Luogen Yao Qian Cao
author_sort	Luogen Yao
title	Anti-periodicity on high-order inertial Hopfield neural networks involving mixed delays
title_short	Anti-periodicity on high-order inertial Hopfield neural networks involving mixed delays
title_full	Anti-periodicity on high-order inertial Hopfield neural networks involving mixed delays
title_fullStr	Anti-periodicity on high-order inertial Hopfield neural networks involving mixed delays
title_full_unstemmed	Anti-periodicity on high-order inertial Hopfield neural networks involving mixed delays
title_sort	anti-periodicity on high-order inertial hopfield neural networks involving mixed delays
publisher	SpringerOpen
series	Journal of Inequalities and Applications
issn	1029-242X
publishDate	2020-07-01
description	Abstract This paper deals with a class of high-order inertial Hopfield neural networks involving mixed delays. Utilizing differential inequality techniques and the Lyapunov function method, we obtain a sufficient assertion to ensure the existence and global exponential stability of anti-periodic solutions of the proposed networks. Moreover, an example with a numerical simulation is furnished to illustrate the effectiveness and feasibility of the theoretical results.
topic	High-order inertial neural networks Anti-periodic solution Global exponential stability Mixed delay
url	http://link.springer.com/article/10.1186/s13660-020-02444-3
work_keys_str_mv	AT luogenyao antiperiodicityonhighorderinertialhopfieldneuralnetworksinvolvingmixeddelays AT qiancao antiperiodicityonhighorderinertialhopfieldneuralnetworksinvolvingmixeddelays
_version_	1724597468125986816

Anti-periodicity on high-order inertial Hopfield neural networks involving mixed delays

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