Global exponential stability of impulsive fuzzy Cohen–Grossberg neural networks with mixed delays and reaction–diffusion terms

This paper is concerned with the problem of exponential stability for a class of impulsive fuzzy Cohen–Grossberg neural networks with mixed time delays and reaction–diffusion. The mixed delays include time-varying delays and continuously distributed delays. Based on the Lyapunov method, Poincaré Integral Inequality, and the linear matrix inequality (LMI) approach, we found some new sufficient conditions ensuring the global exponential stability of equilibrium point for impulsive fuzzy Cohen–Grossberg neural networks with mixed time delays and reaction–diffusion terms. These global exponential stability conditions depend on the reaction–diffusion terms and time delays. The results presented in this paper are less conservative than the existing sufficient stability conditions. Finally, some examples are given to show the effectiveness and superiority of the theoretical results.