Data Prediction in Manufacturing: an Improved Approach Using Least Squares Support Vector Machines

被引：0
|
作者：
Liao, Zaifei [1 ,2 ]
Yang, Tian [1 ,2 ]
Lu, Xinjie [1 ,2 ]
Wang, Hongan [2 ,3 ]
机构：
[1] Chinese Acad Sci, Grad Univ, Beijing, Peoples R China
[2] Chinese Acad Sci, Inst Software, Beijing, Peoples R China
[3] Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing, Peoples R China
来源：
FIRST INTERNATIONAL WORKSHOP ON DATABASE TECHNOLOGY AND APPLICATIONS, PROCEEDINGS | 2009年
关键词：
data quality; data prediction; least squres SVM; Manufacturing; CLASSIFIERS;
D O I：
10.1109/DBTA.2009.21
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Support vector machine (SVM) is a set of related supervised learning methods used for classification and regression based on statistical learning theory. In this paper, we present a least squares support vector machines (LSSVM) regression method based on relative error for manufacturing industries to estimate the true value of imprecise measured data during production logistics process. Our method has already been successfully applied in Manufacturing Execution System (MES) of some petrochemical enterprises in China.
引用
收藏
页码：382 / +
页数：2
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