Anti-periodic dynamics on high-order inertial Hopfield neural networks involving time-varying delays
Taking into accounting time-varying delays and anti-periodic environments, this paper deals with the global convergence dynamics on a class of anti-periodic high-order inertial Hopfield neural networks. First of all, with the help of Lyapunov function method, we prove that the global solutions are e...
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doaj-fd87c11c40704e94beca69f8fc7c915e2020-11-25T03:29:35ZengAIMS PressAIMS Mathematics2473-69882020-07-01565402542110.3934/math.2020347Anti-periodic dynamics on high-order inertial Hopfield neural networks involving time-varying delaysQian Cao0Xiaojin Guo11 College of Mathematics and Physics, Hunan University of Arts and Science, Changde, Hunan 415000, P. R. China2 Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering, Changsha University of Science and Technology, Changsha, 410114, ChinaTaking into accounting time-varying delays and anti-periodic environments, this paper deals with the global convergence dynamics on a class of anti-periodic high-order inertial Hopfield neural networks. First of all, with the help of Lyapunov function method, we prove that the global solutions are exponentially attractive to each other. Secondly, by using analytical techniques in uniform convergence functions sequence, the existence of the anti-periodic solution and its global exponential stability are established. Finally, two examples are arranged to illustrate the effectiveness and feasibility of the obtained results.https://www.aimspress.com/article/10.3934/math.2020347/fulltext.htmlhigh-order inertial neural networksanti-periodic solutionglobal exponential stabilitytime-varying delay |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qian Cao Xiaojin Guo |
spellingShingle |
Qian Cao Xiaojin Guo Anti-periodic dynamics on high-order inertial Hopfield neural networks involving time-varying delays AIMS Mathematics high-order inertial neural networks anti-periodic solution global exponential stability time-varying delay |
author_facet |
Qian Cao Xiaojin Guo |
author_sort |
Qian Cao |
title |
Anti-periodic dynamics on high-order inertial Hopfield neural networks involving time-varying delays |
title_short |
Anti-periodic dynamics on high-order inertial Hopfield neural networks involving time-varying delays |
title_full |
Anti-periodic dynamics on high-order inertial Hopfield neural networks involving time-varying delays |
title_fullStr |
Anti-periodic dynamics on high-order inertial Hopfield neural networks involving time-varying delays |
title_full_unstemmed |
Anti-periodic dynamics on high-order inertial Hopfield neural networks involving time-varying delays |
title_sort |
anti-periodic dynamics on high-order inertial hopfield neural networks involving time-varying delays |
publisher |
AIMS Press |
series |
AIMS Mathematics |
issn |
2473-6988 |
publishDate |
2020-07-01 |
description |
Taking into accounting time-varying delays and anti-periodic environments, this paper deals with the global convergence dynamics on a class of anti-periodic high-order inertial Hopfield neural networks. First of all, with the help of Lyapunov function method, we prove that the global solutions are exponentially attractive to each other. Secondly, by using analytical techniques in uniform convergence functions sequence, the existence of the anti-periodic solution and its global exponential stability are established. Finally, two examples are arranged to illustrate the effectiveness and feasibility of the obtained results. |
topic |
high-order inertial neural networks anti-periodic solution global exponential stability time-varying delay |
url |
https://www.aimspress.com/article/10.3934/math.2020347/fulltext.html |
work_keys_str_mv |
AT qiancao antiperiodicdynamicsonhighorderinertialhopfieldneuralnetworksinvolvingtimevaryingdelays AT xiaojinguo antiperiodicdynamicsonhighorderinertialhopfieldneuralnetworksinvolvingtimevaryingdelays |
_version_ |
1724578181803933696 |