Harmless delays for global asymptotic stability of Cohen–Grossberg neural networks

In this paper, the Cohen–Grossberg neural network models with time delays are considered without assuming the symmetry of connection matrix as well as the monotonicity and differentiability of the activation functions and the self-signal functions. By constructing a novel Lyapunov functional, sufficient criteria for the existence of a unique equilibrium and global asymptotic stability of the network are derived. These criteria are all independent of the magnitudes of the delays, and so the delays under these conditions are harmless. Our results are less conservative and restrictive than previously known results and can be easily verified. In the meantime, our approach does not need to fulfill the rigorous conditions of the amplification functions. It is believed that our results are significant and useful for the design and applications of the Cohen–Grossberg model.