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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 761237, 11 pages
Research Article

Exponential Stability and Numerical Methods of Stochastic Recurrent Neural Networks with Delays

1School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
2Guangdong Electric Power Design Institute, Guangzhou 510663, China

Received 18 April 2013; Accepted 12 July 2013

Academic Editor: Alexander I. Domoshnitsky

Copyright © 2013 Shifang Kuang 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.


Exponential stability in mean square of stochastic delay recurrent neural networks is investigated in detail. By using Itô’s formula and inequality techniques, the sufficient conditions to guarantee the exponential stability in mean square of an equilibrium are given. Under the conditions which guarantee the stability of the analytical solution, the Euler-Maruyama scheme and the split-step backward Euler scheme are proved to be mean-square stable. At last, an example is given to demonstrate our results.