Delay-independent criteria for exponential stability of generalized Cohen-Grossberg neural networks with discrete delays

The global exponential stability is investigated for a class of generalized Cohen-Grossberg neural networks with discrete delays. By means of the combination of the nonlinear measure approach and constructing a novel Lyapunov functional together with some nonlinear functional analysis and inequality techniques, general sufficient conditions are obtained for the existence, uniqueness and global exponential stability of equilibrium of the delayed neural networks, which are mild and independent of the delays. The new criteria do not require the boundedness, monotonicity and differentiability assumptions of the normal and the delayed activation functions. Our results generalize and improve many existing ones.