A novel neural network for nonlinear convex programming.
In this paper, we present a neural network for solving the nonlinear convex programming problem in real time by means of the projection method. The main idea is to convert the convex programming problem into a variational inequality problem. Then a dynamical system and a convex energy function are constructed for resulting variational inequality problem. It is shown that the proposed neural network is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem. Compared with the existing neural networks for solving the nonlinear convex programming problem, the proposed neural network has no Lipschitz condition, no adjustable parameter, and its structure is simple. The validity and transient behavior of the proposed neural network are demonstrated by some simulation results.
Gao
Xing-Bao
XB
College of Mathematics and Information Science, Shaanxi Normal University, Xi'an, Shaanxi 710062, PR China. xin-baog@snnu.edu.cn
D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011381:Programming Languages