Global convergence analysis of impulsive inertial neural networks with time-varying delays

In this paper, a class of impulsive inertial neural networks with time-varying delays is considered. By choosing proper variable transformation, the original inertial neural networks can be rewritten as first-order differential equations. Based on Lyapunov functions method and inequality techniques, some sufficient conditions are derived to guarantee global exponential convergence of the discussed inertial neural networks with impulsive effects. Meanwhile, the framework of the exponential convergence ball in the state space with a pre-specified convergence rate is also given. Here, the existence and uniqueness of the equilibrium points need not to be considered. Finally, some numerical examples with simulation are presented to show the effectiveness of the obtained results.