Discrete Dynamics in Nature and Society
Volume 2011 (2011), Article ID 570295, 16 pages
http://dx.doi.org/10.1155/2011/570295
Research Article

Stability of Stochastic Reaction-Diffusion Recurrent Neural Networks with Unbounded Distributed Delays

Chuangxia Huang,1 Xinsong Yang,2 Yigang He,3 and Lehua Huang1

1College of Mathematics and Computing Science, Changsha University of Science and Technology, Changsha, Hunan 410076, China
2Department of Mathematics, Honghe University, Mengzi, Yunnan 661100, China
3College of Electrical and Information Engineering, Hunan University, Changsha, Hunan 410082, China

Received 5 September 2010; Accepted 11 January 2011

Academic Editor: Binggen Zhang

Copyright © 2011 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.

Abstract

Stability of reaction-diffusion recurrent neural networks (RNNs) with continuously distributed delays and stochastic influence are considered. Some new sufficient conditions to guarantee the almost sure exponential stability and mean square exponential stability of an equilibrium solution are obtained, respectively. Lyapunov's functional method, M-matrix properties, some inequality technique, and nonnegative semimartingale convergence theorem are used in our approach. The obtained conclusions improve some published results.