Modified Gram-Schmidt Method-Based Variable Projection Algorithm for Separable Nonlinear Models

doi:10.1109/TNNLS.2018.2884909

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	Modified Gram-Schmidt Method-Based Variable Projection Algorithm for Separable Nonlinear Models
	Chen, Guang-Yong 1,2,3,4; Gan, Min 1,2,3,4; Ding, Feng 5,6; Chen, C. L. Philip 7,8,9
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2019-08-01
卷号	30 期号:8 页码:2410-2418
通讯作者	Gan, Min(aganmin@aliyun.com)
摘要	Separable nonlinear models are very common in various research fields, such as machine learning and system identification. The variable projection (VP) approach is efficient for the optimization of such models. In this paper, we study various VP algorithms based on different matrix decompositions. Compared with the previous method, we use the analytical expression of the Jacobian matrix instead of finite differences. This improves the efficiency of the VP algorithms. In particular, based on the modified Gram-Schmidt (MGS) method, a more robust implementation of the VP algorithm is introduced for separable nonlinear least-squares problems. In numerical experiments, we compare the performance of five different implementations of the VP algorithm. Numerical results show the efficiency and robustness of the proposed MGS method-based VP algorithm.
关键词	Data fitting modified Gram-Schmidt (MGS) parameter estimation separable nonlinear least-squares problem variable projection (VP)
DOI	10.1109/TNNLS.2018.2884909
关键词[WOS]	LEAST-SQUARES ; IDENTIFICATION ALGORITHM
收录类别	SCI
语种	英语
资助项目	National Nature Science Foundation of China[61673155] ; National Nature Science Foundation of China[61751202] ; National Nature Science Foundation of China[61751205] ; National Nature Science Foundation of China[6157254] ; Technology Innovation Platform Project of Fujian Province[2014H2005] ; Fujian Collaborative Innovation Center for Big Data Application in Governments ; Fujian Engineering Research Center of Big Data Analysis and Processing ; National Nature Science Foundation of China[61673155] ; National Nature Science Foundation of China[61751202] ; National Nature Science Foundation of China[61751205] ; National Nature Science Foundation of China[6157254] ; Technology Innovation Platform Project of Fujian Province[2014H2005] ; Fujian Collaborative Innovation Center for Big Data Application in Governments ; Fujian Engineering Research Center of Big Data Analysis and Processing
项目资助者	National Nature Science Foundation of China ; Technology Innovation Platform Project of Fujian Province ; Fujian Collaborative Innovation Center for Big Data Application in Governments ; Fujian Engineering Research Center of Big Data Analysis and Processing
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000476787300014
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：60[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/27770
专题	离退休人员
通讯作者	Gan, Min
作者单位	1.Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350116, Fujian, Peoples R China 2.Fuzhou Univ, Fujian Prov Key Lab Network Comp & Intelligent In, Fuzhou 350116, Fujian, Peoples R China 3.Minist Educ, Key Lab Spatial Data Min & Informat Sharing, Fuzhou 350116, Fujian, Peoples R China 4.Fuzhou Univ, Ctr Discrete Math & Theoret Comp Sci, Fuzhou 350116, Fujian, Peoples R China 5.Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao 266042, Shandong, Peoples R China 6.Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Jiangsu, Peoples R China 7.Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau 99999, Peoples R China 8.Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China 9.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Chen, Guang-Yong,Gan, Min,Ding, Feng,et al. Modified Gram-Schmidt Method-Based Variable Projection Algorithm for Separable Nonlinear Models[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2019,30(8):2410-2418.
APA	Chen, Guang-Yong,Gan, Min,Ding, Feng,&Chen, C. L. Philip.(2019).Modified Gram-Schmidt Method-Based Variable Projection Algorithm for Separable Nonlinear Models.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,30(8),2410-2418.
MLA	Chen, Guang-Yong,et al."Modified Gram-Schmidt Method-Based Variable Projection Algorithm for Separable Nonlinear Models".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30.8(2019):2410-2418.