A discrete-time recurrent neural network with global exponential stability for constrained linear variational inequalities

In this paper, a discrete-time recurrent neural network with global exponential stability is proposed for solving constrained linear variational inequalities. Compared with the existing neural networks for linear variational inequalities, the proposed neural network in this paper has lower model complexity with only one-layer structure. The global exponential stability of the neural network can be guaranteed under some mild conditions. Simulation results show the performance and characteristics of the proposed neural network.