Discrete Dynamics in Nature and Society
Volume 2008 (2008), Article ID 421614, 14 pages
Research Article

Delay-Dependent Exponential Stability for Discrete-Time BAM Neural Networks with Time-Varying Delays

Yonggang Chen,1 Weiping Bi,2 and Yuanyuan Wu3

1Department of Mathematics, Henan Institute of Science and Technology, Xinxiang 453003, China
2College of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, China
3Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, China

Received 18 May 2008; Revised 5 August 2008; Accepted 10 September 2008

Academic Editor: Yong Zhou

Copyright © 2008 Yonggang Chen et al. 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.


This paper considers the delay-dependent exponential stability for discrete-time BAM neural networks with time-varying delays. By constructing the new Lyapunov functional, the improved delay-dependent exponential stability criterion is derived in terms of linear matrix inequality (LMI). Moreover, in order to reduce the conservativeness, some slack matrices are introduced in this paper. Two numerical examples are presented to show the effectiveness and less conservativeness of the proposed method.