New results for global stability of Cohen–Grossberg neural networks with multiple time delays

This paper studies the global convergence properties of Cohen–Grossberg neural networks with multiple time delays. Without assuming the symmetry of interconnection weight coefficients, and the differentiability and boundedness of activation functions, and by employing Lyapunov functionals, we derive new delay independent sufficient conditions under which a delayed Cohen–Grossberg neural network converges to a unique and globally asymptotically stable equilibrium point. Several examples are given to illustrate the advantages of our results over the previously reported results in the literature.