Study on least squares support vector machines algorithm and its application

	Study on least squares support vector machines algorithm and its application
	Zhang, MG; Li, ZM; Li, WH
	2005
关键词	least squares support vector machines (LS-SVM) SVM identification, soft-sensor modeling
页码	686-688
英文摘要	Support vector machines (SVM) is a novel machine learning method based on small-sample Statistical Learning Theory (SLT), and is powerful for the problem with small sample, nonlinearity, high dimension, and local minima.SVM have been very successful in pattern recognition fault diagnoses and function estimation problems. Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. This paper discusses least squares support vector machines (LS-SVM) estimation algorithm and introduces applications of the novel method for the nonlinear control systems. Then identification of MIMO models and soft-sensor modeling based on least squares support vector machines (LS-SVM) is proposed. The simulation results show that the proposed method provides a powerful tool for identification and soft-sensor modeling and has promising application in industrial process applications.
会议录	ICTAI 2005: 17TH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, PROCEEDINGS
会议录出版者	IEEE COMPUTER SOC
会议录出版地	10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
语种	英语
WOS研究方向	Computer Science
WOS记录号	WOS:000234632500112
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/38303]
专题	电气工程与信息工程学院
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,Li, ZM,Li, WH. Study on least squares support vector machines algorithm and its application[C]. 见:.