Global stability analysis in Cohen–Grossberg neural networks with delays and inverse Hölder neuron activation functions

In this paper, a novel class of Cohen–Grossberg neural networks with delays and inverse Hölder neuron activation functions are presented. By using the topological degree theory and linear matrix inequality (LMI) technique, the existence and uniqueness of equilibrium point for such Cohen–Grossberg neural networks is investigated. By constructing appropriate Lyapunov function, a sufficient condition which ensures the global exponential stability of the equilibrium point is established. Two numerical examples are provided to demonstrate the effectiveness of the theoretical results.