Modified Gram-Schmidt Method-Based Variable Projection Algorithm for Separable Nonlinear Models
文献类型：期刊论文
| 作者 | Chen, Guang-Yong1,2,3,4; Gan, Min1,2,3,4; Ding, Feng5,6; Chen, C. L. Philip7,8,9 |
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2019-08-01 |
| 卷号 | 30期号:8页码:2410-2418 |
| 关键词 | Data fitting modified Gram-Schmidt (MGS) parameter estimation separable nonlinear least-squares problem variable projection (VP) |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2018.2884909 |
| 通讯作者 | Gan, Min(aganmin@aliyun.com) |
| 英文摘要 | 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. |
| WOS关键词 | LEAST-SQUARES ; IDENTIFICATION ALGORITHM |
| 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 |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000476787300014 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/27769]  |
| 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. |
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