Soft-sensor modeling method based on support vector machines

	Soft-sensor modeling method based on support vector machines
	Zhang, MG; Yan, WW
	2005
关键词	soft-sensor modeling support vector machines
页码	208-213
英文摘要	Soft-sensor has been widely used in industrial process control to improve the quality of product and assure safety in production. The core problem of soft-sensor is to construct appropriate mathematic model. Support vector machines (SVM) is a novel machine learning method based on structure risk minimizing principle, which is powerful for the problem characterized by small sample, nonlinearity, high dimension and local minima, and has high generalization. In this paper, soft-sensor modeling method and algorithm based on Support Vector Machines (SVM) is proposed, and cross validation method is used to select hyper-parameters of SVM model. Soft-sensor model based on SVM is applied to estimation nonlinear time-vary system. Experiment results show that SVM provides a new effective method for soft-sensor modeling and has promising application in industrial process applications.
会议录	ICEMI 2005: CONFERENCE PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS, VOL 7
会议录出版者	INTERNATIONAL ACADEMIC PUBLISHERS LTD
会议录出版地	UNIT 1205, 12 FLOOR, SINO PLAZA, 255 GLOUCESTER ROAD, HONG KONG 00000, CAUSEWAY BAY, PEOPLES R CHINA
语种	英语
WOS研究方向	Engineering ; Instruments & Instrumentation
WOS记录号	WOS:000235128600045
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/38345]
专题	电气工程与信息工程学院
通讯作者	Zhang, MG
作者单位	Lanzhou Univ Technol, Sch Elect & Informat Engn, Lanzhou 730050, Peoples R China
推荐引用方式 GB/T 7714	Zhang, MG,Yan, WW. Soft-sensor modeling method based on support vector machines[C]. 见:.