Stability criteria for Cohen-Grossberg neural networks with mixed delays and inverse Lipschitz neuron activations

In this paper, by using topological degree theory and Lyapunov-Krasovskii function method, the problem of stability is investigated for a class of mixed-delayed Cohen-Grossberg neural networks with inverse Lipschitz neuron activations and nonsmooth behaved functions. Several novel delay-dependent sufficient conditions are established towards the existence, uniqueness and global exponential stability of the equilibrium point, which are shown in terms of linear matrix inequalities. Besides, for the case of the activation function satisfying not only the inverse Lipschitz conditions but also the Lipschitz conditions, two criteria are derived by virtue of homeomorphism mapping principle, free-weighting matrix method and Cauchy-Schwarz inequality, which generalize some previous results. Finally, two examples with their simulations are given to show the effectiveness of the theoretical results.