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Sparse representation preserving embedding based on extreme learning machine for process monitoring | |
Hui Yongyong1,2; Zhao Xiaoqiang1,2,3 | |
2020-06 | |
发表期刊 | TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL |
ISSN | 0142-3312 |
卷号 | 42期号:10页码:1895-1907 |
摘要 | Extreme learning machine (ELM) is a fast learning mechanism used in many domains. Unsupervised ELM has improved to extract nonlinear features. A nonlinear dynamic process monitoring method named sparse representation preserving embedding based on ELM (SRPE-ELM) is proposed in this paper. First, the noise is removed by sparse representation and the sparse coefficient is applied to construct the adjacency graph. The adjacency graph with a data-adaptive neighborhood can extract dynamic manifold structure better than a specified neighborhood parameter. Secondly, a new objection function considered the sparse reconstruction and output weights is established to extract nonlinear dynamic manifold structure. Thirdly, the statistic SPE and T-2 based on SRPE-ELM are built to monitor the whole process. Finally, SRPE-ELM is applied in the IRIS data classification example, a numerical case and Tennessee Eastman benchmark process to verify the effectiveness of process monitoring. |
关键词 | Process monitoring extreme learning machine neighborhood preserving embedding sparse representation |
DOI | 10.1177/0142331219898937 |
收录类别 | SCI ; SCIE ; EI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61763029][61873116] ; National Defense Basic Research Project of China[JCKY2018427C002] ; Industrial support and guidance project of colleges and universities in Gansu Province[2019C-05] ; Open fund project of Key Laboratory of Gansu Advanced Control for Industrial Processes[2019KFJJ05] |
WOS研究方向 | Automation & Control Systems ; Instruments & Instrumentation |
WOS类目 | Automation & Control Systems ; Instruments & Instrumentation |
WOS记录号 | WOS:000523847100001 |
出版者 | SAGE PUBLICATIONS LTD |
EI入藏号 | 20201308348685 |
EI主题词 | Process monitoring |
EI分类号 | 723.4 Artificial Intelligence - 913.1 Production Engineering |
来源库 | WOS |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/64315 |
专题 | 电气工程与信息工程学院 |
通讯作者 | Zhao Xiaoqiang |
作者单位 | 1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China; 2.Lanzhou Univ Technol, Key Lab Gansu Adv Control Ind Proc, Lanzhou, Peoples R China; 3.Lanzhou Univ Technol, Natl Expt Teaching Ctr Elect & Control Engn, Lanzhou, Peoples R China |
第一作者单位 | 电气工程与信息工程学院; 兰州理工大学 |
通讯作者单位 | 电气工程与信息工程学院; 兰州理工大学 |
第一作者的第一单位 | 电气工程与信息工程学院 |
推荐引用方式 GB/T 7714 | Hui Yongyong,Zhao Xiaoqiang. Sparse representation preserving embedding based on extreme learning machine for process monitoring[J]. TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL,2020,42(10):1895-1907. |
APA | Hui Yongyong,&Zhao Xiaoqiang.(2020).Sparse representation preserving embedding based on extreme learning machine for process monitoring.TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL,42(10),1895-1907. |
MLA | Hui Yongyong,et al."Sparse representation preserving embedding based on extreme learning machine for process monitoring".TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 42.10(2020):1895-1907. |
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