Global asymptotic stability of Cohen–Grossberg neural networks with constant and variable delays

Global asymptotic stability of Cohen–Grossberg neural networks with constant and variable delays is studied. Some sufficient conditions for the neural networks are proposed to guarantee the global asymptotic convergence by using different Lyapunov functionals. Our criteria represent an extension of the existing results in literatures. A comparison between our results and the previous results admits that our results establish a new set of stability criteria for delayed Cohen–Grossberg neural networks. Those conditions are less restrictive than those given in the earlier reference.