A Modified Neural Networks Approach for Solving Quadratic Programming Problems with equality and inequality constraints

In this paper, a modified neural network model for solving a class of constrained quadratic optimization problems is proposed. It is proved that the proposed neural network is globally stable in the sense of Lyapunov, and the output trajectory of the proposed neural network will converge globally to an optimal solution. The validity and transient behavior of the proposed neural network are demonstrated by using two numerical examples

Keywords:

dynamical system , strictly convex quadratic programming , stability , global convergence , recurrent neural network.