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Discrete Dynamics in Nature and Society
Volume 2012, Article ID 168375, 23 pages
Research Article

Global Exponential Stability of Antiperiodic Solutions for Discrete-Time Neural Networks with Mixed Delays and Impulses

Department of Mathematics, Chongqing Jiaotong University, Chongqing 400074, China

Received 21 October 2011; Accepted 25 December 2011

Academic Editor: Taher S. Hassan

Copyright © 2012 Xiaofeng Chen and Qiankun Song. 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.


The problem on global exponential stability of antiperiodic solution is investigated for a class of impulsive discrete-time neural networks with time-varying discrete delays and distributed delays. By constructing an appropriate Lyapunov-Krasovskii functional, and using the contraction mapping principle and the matrix inequality techniques, a new delay-dependent criterion for checking the existence, uniqueness, and global exponential stability of anti-periodic solution is derived in linear matrix inequalities (LMIs). Two simulation examples are given to show the effectiveness of the proposed result.