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

Author

Liao, Zaifei ; Yang, Tian ; Lu, Xinjie ; Wang, Hongan

fYear

2009

fDate

25-26 April 2009

Firstpage

382

Lastpage

385

Abstract

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.

Keywords

learning (artificial intelligence); least squares approximations; logistics data processing; manufacturing data processing; petrochemicals; regression analysis; support vector machines; data prediction; least squares support vector machines regression method; manufacturing execution system; manufacturing industry; petrochemical enterprises; production logistics process; statistical learning theory; supervised learning methods; Least squares approximation; Least squares methods; Logistics; Manufacturing; Petrochemicals; Production; Statistical learning; Supervised learning; Support vector machine classification; Support vector machines; data prediction; data quality; least squres SVM; manufacturing;

fLanguage

English

Publisher

ieee

Conference_Titel

Database Technology and Applications, 2009 First International Workshop on

Conference_Location

Wuhan, Hubei

Print_ISBN

978-0-7695-3604-0

Type

conf

DOI

10.1109/DBTA.2009.21

Filename

5207737