Novel exponential stability of reaction-diffusion cohen-grossberg neural networks

Global exponential stability problem is studied for a class of continuous-time reaction-diffusion Cohen-Grossberg neural networks with distributed delays. By decomposing the distributed-delay matrix and using matrix inequality technique and under some suitable assumptions on amplification function, a novel delay-kernel-dependent exponential stability condition for the equilibrium point of reaction-diffusion Cohen-Grossberg neural networks with distributed delays, and the delay kernel information and the amplification function information are completely involved in the stability condition. The obtained results are easy to check and some of them are less conservative than the existing results in the literatures. Some remarks are given to show the advantages over the previous results.