Novel Criteria on Global Robust Exponential Stability to a Class of Reaction‐Diffusion Neural Networks with Delays
Jie Pan, Shouming Zhong · Discrete Dynamics in Nature and Society · 2009
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.