A Recurrent Neural Network for Solving Strictly Convex Quadratic Programming Problems

In this paper, we present an improved neural network to solve strictly convex quadratic program-ming(qp) problem. the proposed model includes a set of differential equations such that their equi-librium points correspond to optimality condition of convex (qp) problem and has a lower structurecomplexity respect to the other existing neural network model for solving such problems. in theoret-ical aspect, stability and global convergence of the proposed neural network is proved. the validityand transient behavior of the proposed neural network are demonstrated by using four numericalexamples.