A class of linearly constrained linear variational inequalities is considered. Two neural networks for solving it are proposed by transforming it into the equivalent equations. The proposed models are proved to be Liapunov stable and globally converge to an solution of the underlying problem. Moreover, the global exponential stability of the proposed models are shown under certain conditions.The size of each proposed models is the same as that of the underlying problems, and the network parameter is easy to be chosen.The feasibility and effectiveness of the proposed neural networks are supported by the simulation experiments.

linear variational inequality; neural network; global exponential stability; convergence