Global exponential stability of nonautonomous neural network models with unbounded delays.

For a nonautonomous class of n-dimensional differential system with infinite delays, we give sufficient conditions for its global exponential stability, without showing the existence of an equilibrium point, or a periodic solution, or an almost periodic solution. We apply our main result to several concrete neural network models, studied in the literature, and a comparison of results is given. Contrary to usual in the literature about neural networks, the assumption of bounded coefficients is not required to obtain the global exponential stability. Finally, we present numerical examples to illustrate the effectiveness of our results.