One-layer neural network for solving least absolute deviation problem with box and equality constraints

被引：3

作者：

Li, Cuiping ^{[1
,2
]}

Gao, Xingbao ^{[1
]}

机构：

[1] Shaanxi Normal Univ, Sch Math & Informat Sci, Xian 710119, Shaanxi, Peoples R China

[2] Northwest Univ Polit Sci & Law, Sch Econ, Xian 710122, Shaanxi, Peoples R China

来源：

NEUROCOMPUTING | 2019年 / 330卷

基金：

中国国家自然科学基金;

关键词：

Least absolute deviation; Neural network; One-layer; Lyapunov stable; L(1); L(2);

D O I：

10.1016/j.neucom.2018.11.037

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a neural network for solving least absolute deviation problems with equality and box constraints. Compared with some existing models, the proposed neural network has fewer state variables and only one-layer structure. The proposed model is proved to be Lyapunov stable and converge to an exact optimal solution of the original problem. Some simulation results show the validity and transient behavior of the proposed neural network. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：483 / 489

页数：7

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