A recurrent neural network for solving a class of general variational inequalities

Abstract—This paper presents a recurrent neural-network model for solving a special class of general variational inequalities (GVIs), which includes classical VIs as special cases. It is proved that the proposed neural network (NN) for solving this class of GVIs can be globally convergent, globally asymptotically stable, and globally exponentially stable under different conditions. The proposed NN can be viewed as a modified version of the gen-eral projection NN existing in the literature. Several numerical examples are provided to demonstrate the effectiveness and per-formance of the proposed NN. Index Terms—General projection neural network (GPNN), gen-eral variational inequalities (GVIs), global asymptotic stability, global exponential stability, recurrent neural network. I