Global exponential stability analysis of Cohen-Grossberg neural networks

In this paper, we investigate the global exponential stability of the Cohen-Grossberg neural networks with time delays, we derive a general sufficient condition ensuring global stability of the neural networks by constructing a novel Lyapunov functional and carefully estimating its derivative. The main advantage of the proposed condition is the drop of the absolute symbol from the absolute values of the self feedback connection weights, thus improves some existing conditions. As a result, some stability conditions for the Cohen-Grossberg neural network without delays are also derived, which generalize and unify some previous results.