Global Exponential Stability of Delayed Cohen-Grossberg Neural Networks

In this paper, using a fixed-point theorem and re-duction to absurdity, we have obtained some suffi-cient conditions to guarantee that Cohen-Grossberg neural networks with discrete and distributed delays are globally exponentially stable. Since our model is more general and our assumptions relax the previ-ous assumptions in some existing works, the results presented in this paper are the improvement and ex-tension of the existed ones. Finally, the validity and performance of the results are illustrated by two sim-ulation examples.