Linear simultaneous equations' neural solution and its application to convex quadratic programming with equality-constraint

A gradient-based neural network (gnn) is improved and presented for the linear algebraic equation solving. then,such a gnn model is used for the online solution of the convex quadratic programming (qp) with equality-constraints under the usage of lagrangian function and karush-kuhn-tucker (kkt) condition. according to the electronic architecture of such a gnn,it is known that the performance of the presented gnn could be enhanced by adopting different activation function arrays and/or design parameters. computer simulation results substantiate that such a gnn could obtain the accurate solution of the qp problem with an effective manner. © 2013 yuhuan chen et al.