Potential Effects of Delay on the Stability of a Class of Impulsive Neural Networks

Aiming at the interference of the delay term in continuous dynamics to the impulsive systems, we study the potential effects of time delay on the stability of a class of impulsive neural networks (INNs) in this paper. Two cases of delay are considered. For the case of small delay, a sufficient condi...

Full description

Saved in:
Bibliographic Details
Main Authors: Nan Zhan, Ailong Wu
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2022/6673618
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832551149452918784
author Nan Zhan
Ailong Wu
author_facet Nan Zhan
Ailong Wu
author_sort Nan Zhan
collection DOAJ
description Aiming at the interference of the delay term in continuous dynamics to the impulsive systems, we study the potential effects of time delay on the stability of a class of impulsive neural networks (INNs) in this paper. Two cases of delay are considered. For the case of small delay, a sufficient condition for the stability of delayed INNs is obtained by virtue of the average impulsive interval (AII) method. The derived results illustrate that within limits, the convergence rate of the system becomes larger with the increase of time delay. For another case, a strict comparison principle is proposed to prove that the impulsive system still maintains the original stability for any large but bounded delay under certain conditions. In particular, as an extension, the stability of delayed INNs for hybrid impulses containing both stabilizing and destabilizing impulses is also discussed. Finally, three examples are simulated to demonstrate the validity of the theoretical results.
format Article
id doaj-art-d6616c7dacbc44ddb0d562ed06c58ea9
institution Kabale University
issn 1099-0526
language English
publishDate 2022-01-01
publisher Wiley
record_format Article
series Complexity
spelling doaj-art-d6616c7dacbc44ddb0d562ed06c58ea92025-02-03T06:04:49ZengWileyComplexity1099-05262022-01-01202210.1155/2022/6673618Potential Effects of Delay on the Stability of a Class of Impulsive Neural NetworksNan Zhan0Ailong Wu1College of Mathematics and StatisticsCollege of Mathematics and StatisticsAiming at the interference of the delay term in continuous dynamics to the impulsive systems, we study the potential effects of time delay on the stability of a class of impulsive neural networks (INNs) in this paper. Two cases of delay are considered. For the case of small delay, a sufficient condition for the stability of delayed INNs is obtained by virtue of the average impulsive interval (AII) method. The derived results illustrate that within limits, the convergence rate of the system becomes larger with the increase of time delay. For another case, a strict comparison principle is proposed to prove that the impulsive system still maintains the original stability for any large but bounded delay under certain conditions. In particular, as an extension, the stability of delayed INNs for hybrid impulses containing both stabilizing and destabilizing impulses is also discussed. Finally, three examples are simulated to demonstrate the validity of the theoretical results.http://dx.doi.org/10.1155/2022/6673618
spellingShingle Nan Zhan
Ailong Wu
Potential Effects of Delay on the Stability of a Class of Impulsive Neural Networks
Complexity
title Potential Effects of Delay on the Stability of a Class of Impulsive Neural Networks
title_full Potential Effects of Delay on the Stability of a Class of Impulsive Neural Networks
title_fullStr Potential Effects of Delay on the Stability of a Class of Impulsive Neural Networks
title_full_unstemmed Potential Effects of Delay on the Stability of a Class of Impulsive Neural Networks
title_short Potential Effects of Delay on the Stability of a Class of Impulsive Neural Networks
title_sort potential effects of delay on the stability of a class of impulsive neural networks
url http://dx.doi.org/10.1155/2022/6673618
work_keys_str_mv AT nanzhan potentialeffectsofdelayonthestabilityofaclassofimpulsiveneuralnetworks
AT ailongwu potentialeffectsofdelayonthestabilityofaclassofimpulsiveneuralnetworks