data prediction in manufacturing: an improved approach using least squares support vector machines

	data prediction in manufacturing: an improved approach using least squares support vector machines
	Liao Zaifei; Yang Tian; Lu Xinjie; Wang Hongan
	2009
Conference Name	2009 1st International Workshop on Database Technology and Applications, DBTA 2009
Source	Proceedings - 2009 1st International Workshop on Database Technology and Applications, DBTA 2009
Conference Date	April 25,
Conference Place	Wuhan, Hubei, China
Indexed Type	EI
Publish Place	United States
ISBN	9780769536040
Department	(1) Graduate University, Chinese Academy of Sciences, Beijing, China; (2) Institute of Software, Chinese Academy of Sciences, Beijing, China; (3) State Key Lab. of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
English 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. © 2009 IEEE.
Keyword	Gears Manufacture Multilayer Neural Networks
Sponsorship	Wuhan University of Science and Technology; Huazhong University of Science and Technology; Huazhong Normal University; Harbin Institute of Technology; Wuhan University; I and M/CI Joint Chapter of IEEE Ukraine Section
Content Type	会议论文
URI	http://ir.iscas.ac.cn/handle/311060/8412
Collection	人机交互技术与智能信息处理实验室
Recommended Citation GB/T 7714	Liao Zaifei,Yang Tian,Lu Xinjie,et al. data prediction in manufacturing: an improved approach using least squares support vector machines[C]. United States,2009.

Files in This Item:
There are no files associated with this item.

If you have any objections to this item, please fill out the form below and the administrator will contact you as soon as possible.
Content:
Email：	*
Institution:
Verification Code:	Refresh

Any comments and suggestions are welcomed.
Title:	*
Content:
Email：	*
Verification Code:	Refresh

中国科学院软件研究所机构知识库