Modeling and optimization of high-technology manufacturing productivity

被引：0
|
作者：
Xu, Sheng [1 ]
Zhao, Hui-Fang
Sun, Zhao-Hua
Bao, Xiao-Hua
机构：
[1] Hefei Univ Technol, Sch Management, Hefei 230009, Peoples R China
[2] Xi An Jiao Tong Univ, Sch Management, Xian 710049, Peoples R China
[3] Hefei Univ Technol, Sch Elect Engn & Automatizat, Hefei 230009, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS | 2006年 / 3973卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
As more and more industries experience the globalization of business activities, measuring productivity performance has become an area of concern for companies and policy makers in Europe, the United States, Japan and so on. A novel way about nonlinear regression modeling of high-technology manufacturing (HTM) productivity with the support vector machines (SVM) is presented in this paper. Optimization of labor productivity (LP) is also presented in this paper, which is based on chaos and uses the SVM regression model as the objective function.
引用
收藏
页码：914 / 920
页数：7
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