A New Criterion of Global Robust Stability for the Static Neural Network with Time-Delays

The dynamics of the local field neural networks have been studied extensively, and many good results have been obtained. It should be pointed out that, the global robust stability for the static neural networks with time-delays received very little attention despite its practical importance. In this paper, by using the topological degree theory M-matrix theory and the linear differential inequality technique, a new criterion of global robust stability for static neural networks with time-delays is derived. An example is exploited to show the usefulness of the derived stability conditions.