A new gradient-based neural network for solving quadratic programs
Xingbao Gao · Journal of Shaanxi Normal University · 2004
A new gradient-based neural network for solving convex quadratic programming problems is proposed by means of the inherent properties of the original problem. The proposed model is strictly proved to be Liapunov stable and can asymptotically converge to an exact optimal solution. Moreover, the global exponential stability of the proposed model is also developed. There is no need for choosing the self-feedback or lateral connection matrices in the proposed model, and its size is less than that of original problem. The feasibility and effectiveness of the proposed neural network are supported by the simulation experiments.