A high-performance neural network for solving linear and quadratic programming problems

被引：65
|
作者：
Wu, XY
Xia, YS
Li, JM
Chen, WK
机构：
[1] UNIV ILLINOIS,DEPT MECH ENGN,CHICAGO,IL 60607
[2] UNIV ILLINOIS,DEPT ELECT ENGN & COMP SCI,CHICAGO,IL 60607
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 1996年 / 7卷 / 03期
关键词：
D O I：
10.1109/72.501722
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Two classes of high-performance neural networks for solving linear and quadratic programming problems are given. We prove that the new system converges globally to the solutions of the linear and quadratic programming problems, In a neural network, network parameters are usually not specified. The proposed models can overcome numerical difficulty caused by neural networks with network parameters and obtain desired approximate solutions of the linear and quadratic programming problems.
引用
收藏
页码：643 / 651
页数：9
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