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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 138379, 11 pages
http://dx.doi.org/10.1155/2014/138379
Research Article

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

1School of Mathematics and Information, Shanghai Lixin University of Commerce, Shanghai 201620, China
2College of Mathematics, Physics and Information Engineering, Jiaxing University, Jiaxing, Zhejiang 314001, China

Received 27 August 2013; Accepted 11 January 2014; Published 19 March 2014

Academic Editor: Douglas Anderson

Copyright © 2014 Wei Chen and Shuhua Gong. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.