Table of Contents
ISRN Discrete Mathematics
Volume 2011, Article ID 153409, 23 pages
Research Article

Exponential Stability for Discrete-Time Stochastic BAM Neural Networks with Discrete and Distributed Delays

1Department of Mathematics, Periyar University, Salem 636 011, India
2Department of Mathematics, Sungkyunkwan University, Suwon 440-746, Republic of Korea
3Department of Mathematics, Anna University of Technology, Coimbatore 641 047, India

Received 13 September 2011; Accepted 23 October 2011

Academic Editors: C.-K. Lin and N. I. Trinajstić

Copyright © 2011 R. Raja 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 deals with the stability analysis problem for a class of discrete-time stochastic BAM neural networks with discrete and distributed time-varying delays. By constructing a suitable Lyapunov-Krasovskii functional and employing M-matrix theory, we find some sufficient conditions ensuring the global exponential stability of the equilibrium point for stochastic BAM neural networks with time-varying delays. The conditions obtained here are expressed in terms of LMIs whose feasibility can be easily checked by MATLAB LMI Control toolbox. A numerical example is presented to show the effectiveness of the derived LMI-based stability conditions.