A simple and high performance neural network for quadratic programming problems

被引：28
|
作者：
Tao, Q [1 ]
Cao, JD
Sun, DM
机构：
[1] Univ Sci & Technol China, Dept Automat, Hefei 230027, Peoples R China
[2] Southeast Univ, Dept Appl Math, Nanjing 210096, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2001年 / 124卷 / 02期
关键词：
neural networks; quadratic programming problems; global convergence; constraint gradient; feasible solution;
D O I：
10.1016/S0096-3003(00)00097-7
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, a neural network for quadratic programming problems is simplified. The simplicity is necessary for the high accuracy of solutions and low cost of implementation. The proposed network is proved to be an extension of Newton's optimal descent flow about constraints problems and is globally convergent. The network dynamic behaviors are also discussed and these can get the feasible solution more easily. The simulations demonstrate the reasonability of the theory and advantages of the network. (C) 2001 Elsevier Science Inc. All rights reserved.
引用
收藏
页码：251 / 260
页数：10
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