School of Automation, Southeast University, Nanjing, China.
Neural Comput. 2010 Nov;22(11):2962-78. doi: 10.1162/NECO_a_00029.
In this letter, a novel recurrent neural network based on the gradient method is proposed for solving linear programming problems. Finite-time convergence of the proposed neural network is proved by using the Lyapunov method. Compared with the existing neural networks for linear programming, the proposed neural network is globally convergent to exact optimal solutions in finite time, which is remarkable and rare in the literature of neural networks for optimization. Some numerical examples are given to show the effectiveness and excellent performance of the new recurrent neural network.
在这封信中，提出了一种基于梯度法的新型递归神经网络，用于解决线性规划问题。通过 Lyapunov 方法证明了所提出的神经网络的有限时间收敛性。与现有的线性规划神经网络相比，所提出的神经网络在有限时间内全局收敛到精确最优解，这在优化神经网络文献中是显著的和罕见的。给出了一些数值例子，以显示新的递归神经网络的有效性和优异性能。
