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Mathematical Problems in Engineering
Volume 2014, Article ID 486052, 17 pages
http://dx.doi.org/10.1155/2014/486052
Research Article

Preserving Global Exponential Stability of Hybrid BAM Neural Networks with Reaction Diffusion Terms in the Presence of Stochastic Noise and Connection Weight Matrices Uncertainty

Yan Li1,2 and Yi Shen1

1Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2College of Science, Huazhong Agriculture University, Wuhan 430079, China

Received 8 February 2014; Accepted 6 March 2014; Published 17 April 2014

Academic Editor: Weihai Zhang

Copyright © 2014 Yan Li and Yi Shen. 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

We study the impact of stochastic noise and connection weight matrices uncertainty on global exponential stability of hybrid BAM neural networks with reaction diffusion terms. Given globally exponentially stable hybrid BAM neural networks with reaction diffusion terms, the question to be addressed here is how much stochastic noise and connection weights matrices uncertainty the neural networks can tolerate while maintaining global exponential stability. The upper threshold of stochastic noise and connection weights matrices uncertainty is defined by using the transcendental equations. We find that the perturbed hybrid BAM neural networks with reaction diffusion terms preserve global exponential stability if the intensity of both stochastic noise and connection weights matrices uncertainty is smaller than the defined upper threshold. A numerical example is also provided to illustrate the theoretical conclusion.