A recurrent neural network for solving nonlinear convex programs subject to linear constraints.
In this paper, we propose a recurrent neural network for solving nonlinear convex programming problems with linear constraints. The proposed neural network has a simpler structure and a lower complexity for implementation than the existing neural networks for solving such problems. It is shown here that the proposed neural network is stable in the sense of Lyapunov and globally convergent to an optimal solution within a finite time under the condition that the objective function is strictly convex. Compared with the existing convergence results, the present results do not require Lipschitz continuity condition on the objective function. Finally, examples are provided to show the applicability of the proposed neural network.
Xia
Youshen
Y
Department of Applied Mathematics, Nanjing University of Posts and Telecommunications, Nanjing 210003, China. ysxia2001@yahoo.com
Wang
Jun
J
D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics