Global exponential stability of delayed Cohen-Grossberg neural networks

In this paper, using a fixed-point theorem and reduction to absurdity, the authors have obtained some sufficient conditions to guarantee that Cohen-Grossberg neural networks with discrete and distributed delays are globally exponentially stable. Since the model is more general and the assumptions relax the previous assumptions in some existing works, the results presented in this paper are the improvement and extension of the existed ones. Finally, the validity and performance of the results are illustrated by two simulation examples.