Input-to-state stability of delayed reaction-diffusion neural networks with impulsive effects
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2018 Elsevier B.V. In this article, we study input-to-state stability (ISS) of impulsive reaction-diffusion neural networks (RDNNs) with variable delays. Firstly, a class of impulsive RDNNs with variable delays is formulated, in which impulsive functions are nonlinear. Secondly, by constructing an impulsive delay differential inequality with variable inputs, we derive several novel criteria on ISS for the desired impulsive neural networks with reaction-diffusion term. Moreover, they are unveiled not only that the ISS of the RDNNs does not change under small perturbations of impulses, but also the impulsive effects can successfully stabilize the system even when the corresponding non-impulsive system is unstable. Finally, two examples and their simulations are provided to illustrate the effectiveness of our results and show how impulses affect ISS of the RDNNs.