An analysis of global asymptotic stability of delayed Cohen-Grossberg neural networks via nonsmooth analysis

In this paper, using a method based on nonsmooth analysis and the Lyapunov method, several new sufficient conditions are derived to ensure existence and global asymptotic stability of the equilibrium point for delayed Cohen-Grossberg neural networks. The obtained criteria can be checked easily in practice and have a distinguished feature from previous studies, and our results do not need the smoothness of the behaved function, boundedness of the activation function and the symmetry of the connection matrices. Moreover, two examples are exploited to illustrate the effectiveness of the proposed criteria in comparison with some existing results.