An Algebraic Criterion for Global Exponential Stability of Cohen-Grossberg Neural Networks with Time-varying Delays

In this paper, by constructing an appropriate Lyapunov functional, sufficient criteria independent of the delays for global exponential stability of the network are derived. The algebra criteria are applicable for other neural network models. This results are less conservative and restrictive than previously known results and can be easily verified. And the result has overcome the obvious drawback that previous works neglect the signs of the connecting weights, and thus, do not distinguish the differences between excitatory and inhibitory connections. It is believed that the results are significant and useful for the design and applications of the Cohen-Grossberg model.