Stability in Lagrange sense for Cohen–Grossberg neural networks with time-varying delays and finite distributed delays

In this paper, we study the global exponential stability in Lagrange sense for a class of Cohen–Grossberg neural networks with time-varying delays and finite distributed delays. Based on the Lyapunov stability theory, several global exponential attractive sets in which all trajectories converge are obtained. We analyze three different types of activation functions which include both bounded and unbounded activation functions. These results can also be applied to analyze monostable as well as multistable and more extensive neural networks due to making no assumptions on the number of equilibria. Meanwhile, the results obtained in this paper are more general and challenging than that of the existing references. Finally, one example is given and analyzed to verify our results.