Global Exponential Stability of Antiperiodic Solution for Impulsive High-Order Hopfield Neural Networks

This paper is concerned with antiperiodic solutions for impulsive high-order Hopfield neural networks with leakage delays and continuously distributed delays. By employing a novel proof, some sufficient criteria are established to ensure the existence and global exponential stability of the antiperi...

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Main Authors: Wei Chen, Shuhua Gong
Format: Article
Language:English
Published: Wiley 2014-01-01
Series:Abstract and Applied Analysis
Online Access:http://dx.doi.org/10.1155/2014/138379
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author Wei Chen
Shuhua Gong
author_facet Wei Chen
Shuhua Gong
author_sort Wei Chen
collection DOAJ
description This paper is concerned with antiperiodic solutions for impulsive high-order Hopfield neural networks with leakage delays and continuously distributed delays. By employing a novel proof, some sufficient criteria are established to ensure the existence and global exponential stability of the antiperiodic solution, which are new and complement of previously known results. Moreover, an example and numerical simulations are given to support the theoretical result.
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publishDate 2014-01-01
publisher Wiley
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series Abstract and Applied Analysis
spelling doaj-art-4cc6afc9f8434df0a65ff3b25ce961112025-08-20T02:38:39ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/138379138379Global Exponential Stability of Antiperiodic Solution for Impulsive High-Order Hopfield Neural NetworksWei Chen0Shuhua Gong1School of Mathematics and Information, Shanghai Lixin University of Commerce, Shanghai 201620, ChinaCollege of Mathematics, Physics and Information Engineering, Jiaxing University, Jiaxing, Zhejiang 314001, ChinaThis paper is concerned with antiperiodic solutions for impulsive high-order Hopfield neural networks with leakage delays and continuously distributed delays. By employing a novel proof, some sufficient criteria are established to ensure the existence and global exponential stability of the antiperiodic solution, which are new and complement of previously known results. Moreover, an example and numerical simulations are given to support the theoretical result.http://dx.doi.org/10.1155/2014/138379
spellingShingle Wei Chen
Shuhua Gong
Global Exponential Stability of Antiperiodic Solution for Impulsive High-Order Hopfield Neural Networks
Abstract and Applied Analysis
title Global Exponential Stability of Antiperiodic Solution for Impulsive High-Order Hopfield Neural Networks
title_full Global Exponential Stability of Antiperiodic Solution for Impulsive High-Order Hopfield Neural Networks
title_fullStr Global Exponential Stability of Antiperiodic Solution for Impulsive High-Order Hopfield Neural Networks
title_full_unstemmed Global Exponential Stability of Antiperiodic Solution for Impulsive High-Order Hopfield Neural Networks
title_short Global Exponential Stability of Antiperiodic Solution for Impulsive High-Order Hopfield Neural Networks
title_sort global exponential stability of antiperiodic solution for impulsive high order hopfield neural networks
url http://dx.doi.org/10.1155/2014/138379
work_keys_str_mv AT weichen globalexponentialstabilityofantiperiodicsolutionforimpulsivehighorderhopfieldneuralnetworks
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