LMI-based stability analysis of impulsive high-order Hopfield-type neural networks

This paper investigates the asymptotic stability of a class of impulsive high-order neural networks, which can be considered as an expansion of Hopfield neural networks. By employing Lyapunov functions and linear matrix inequality (LMI) technique, sufficient conditions that guarantee the global asymptotic stability of the equilibrium point are derived. The proposed criteria are easily verified and possess many adjustable parameters, which provide flexibility for the analysis of the neural networks. Finally, two examples are given to show the effectiveness of the proposed results.