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Discrete Dynamics in Nature and Society
Volume 2009, Article ID 291594, 14 pages
Research Article

Novel Criteria on Global Robust Exponential Stability to a Class of Reaction-Diffusion Neural Networks with Delays

1College of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
2Department of Applied Mathematics, Sichuan Agricultural University, Yaan, Sichuan 625014, China

Received 13 June 2009; Accepted 31 August 2009

Academic Editor: Manuel De La Sen

Copyright © 2009 Jie Pan and Shouming Zhong. 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.


The global exponential robust stability is investigated to a class of reaction-diffusion Cohen-Grossberg neural network (CGNNs) with constant time-delays, this neural network contains time invariant uncertain parameters whose values are unknown but bounded in given compact sets. By employing the Lyapunov-functional method, several new sufficient conditions are obtained to ensure the global exponential robust stability of equilibrium point for the reaction diffusion CGNN with delays. These sufficient conditions depend on the reaction-diffusion terms, which is a preeminent feature that distinguishes the present research from the previous research on delayed neural networks with reaction-diffusion. Two examples are given to show the effectiveness of the obtained results.