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.