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Discrete Dynamics in Nature and Society
Volume 2010, Article ID 513218, 15 pages
Research Article

Mean Square Exponential Stability of Stochastic Cohen-Grossberg Neural Networks with Unbounded Distributed Delays

1College of Mathematics and Computing Science, Changsha University of Science and Technology, Changsha, Hunan 410114, China
2College of Electrical and Information Engineering, Hunan University, Changsha, Hunan 410082, China

Received 5 July 2010; Accepted 1 September 2010

Academic Editor: Juan J. Nieto

Copyright © 2010 Chuangxia Huang 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 addresses the issue of mean square exponential stability of stochastic Cohen-Grossberg neural networks (SCGNN), whose state variables are described by stochastic nonlinear integrodifferential equations. With the help of Lyapunov function, stochastic analysis technique, and inequality techniques, some novel sufficient conditions on mean square exponential stability for SCGNN are given. Furthermore, we also establish some sufficient conditions for checking exponential stability for Cohen-Grossberg neural networks with unbounded distributed delays.