Xia Youshen, Feng Gang, Wang Jun
Department of Manufacturing Engineering and Engineering Management, The City University of Hong Kong, Hong Kong, China.
Neural Netw. 2004 Sep;17(7):1003-15. doi: 10.1016/j.neunet.2004.05.006.
This paper presents a recurrent neural network for solving strict convex quadratic programming problems and related linear piecewise equations. Compared with the existing neural networks for quadratic program, the proposed neural network has a one-layer structure with a low model complexity. Moreover, the proposed neural network is shown to have a finite-time convergence and exponential convergence. Illustrative examples further show the good performance of the proposed neural network in real-time applications.
本文提出了一种用于求解严格凸二次规划问题及相关线性分段方程的递归神经网络。与现有的用于二次规划的神经网络相比，所提出的神经网络具有单层结构，模型复杂度较低。此外，所提出的神经网络被证明具有有限时间收敛性和指数收敛性。示例进一步展示了所提出的神经网络在实时应用中的良好性能。
