期刊
NEURAL NETWORKS
卷 51, 期 -, 页码 17-25出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2013.11.015
关键词
Neural network; Convex quadratic bilevel programming problems; Nonautonomous differential inclusions; Nonsmooth analysis
资金
- NPRP from Qatar National Research Fund (a member of Qatar Foundation) [NPRP 4-1162-1-181]
- Natural Science Foundation of China [61374078]
In this paper, using the idea of successive approximation, we propose a neural network to solve convex quadratic bilevel programming problems (CQBPPs), which is modeled by a nonautonomous differential inclusion. Different from the existing neural network for CQBPP, the model has the least number of state variables and simple structure. Based on the theory of nonsmooth analysis, differential inclusions and Lyapunov-like method, the limit equilibrium points sequence of the proposed neural networks can approximately converge to an optimal solution of CQBPP under certain conditions. Finally, simulation results on two numerical examples and the portfolio selection problem show the effectiveness and performance of the proposed neural network. (C) 2013 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据