Unsupervised Regression Model of Geodesic Flow Kernel Based on Dynamic Independent Component Analysis and Dynamic Principal Component Analysis

It is difficult to accurately measure parameters by using the traditional soft sensor algorithm when the working condition of industrial process is changed. Therefore, a transfer learning strategy is introduced based on geodesic flow kernel to solve this problem. At the same time, the method is opti...

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Autores principales:	LAI Yanbo, YAN Gaowei, CHENG Lan, CHEN Zehua
Formato:	article
Lenguaje:	ZH
Publicado:	Editorial Office of Journal of Shanghai Jiao Tong University 2020
Materias:	soft sensor geodesic flow kernel dynamic characteristics dynamic independent component analysis dynamic principal component analysis Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989
Acceso en línea:	https://doaj.org/article/a55168d826a44c0ab572a2137dbea08a
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spelling	oai:doaj.org-article:a55168d826a44c0ab572a2137dbea08a2021-11-04T09:34:51ZUnsupervised Regression Model of Geodesic Flow Kernel Based on Dynamic Independent Component Analysis and Dynamic Principal Component Analysis1006-246710.16183/j.cnki.jsjtu.2020.171https://doaj.org/article/a55168d826a44c0ab572a2137dbea08a2020-12-01T00:00:00Zhttp://xuebao.sjtu.edu.cn/CN/10.16183/j.cnki.jsjtu.2020.171https://doaj.org/toc/1006-2467It is difficult to accurately measure parameters by using the traditional soft sensor algorithm when the working condition of industrial process is changed. Therefore, a transfer learning strategy is introduced based on geodesic flow kernel to solve this problem. At the same time, the method is optimized to solve the problems of dynamic characteristic extraction and incomplete Gaussian distribution in industrial process. The augmented matrix is first constructed to deal with the dynamic characteristics of the process. Independent component analysis and principal component analysis are performed on the processed data to extract the non-Gaussian and Gaussian information of the source domain and the target domain. Then, the non-Gaussian and Gaussian information of the source domain is adapted to the target domain respectively on the Grassmann manifold. Finally, the maximum mean discrepancy is used to measure the distribution between the source domain and the target domain after domain adaptation, and the calculated results are applied to construct the weight of the model based on the source domain after domain adaptation. The results show that the method not only reduces the difference of distribution between the source domain and the target domain, but also solves the problems of dynamic characteristic extraction and incomplete Gaussian distribution in industrial process. The validity and the practicability of the model are proved by experiments on Tennessee Eastman data.LAI YanboYAN GaoweiCHENG LanCHEN ZehuaEditorial Office of Journal of Shanghai Jiao Tong Universityarticlesoft sensorgeodesic flow kerneldynamic characteristicsdynamic independent component analysisdynamic principal component analysisEngineering (General). Civil engineering (General)TA1-2040Chemical engineeringTP155-156Naval architecture. Shipbuilding. Marine engineeringVM1-989ZHShanghai Jiaotong Daxue xuebao, Vol 54, Iss 12, Pp 1269-1277 (2020)
institution	DOAJ
collection	DOAJ
language	ZH
topic	soft sensor geodesic flow kernel dynamic characteristics dynamic independent component analysis dynamic principal component analysis Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989
spellingShingle	soft sensor geodesic flow kernel dynamic characteristics dynamic independent component analysis dynamic principal component analysis Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989 LAI Yanbo YAN Gaowei CHENG Lan CHEN Zehua Unsupervised Regression Model of Geodesic Flow Kernel Based on Dynamic Independent Component Analysis and Dynamic Principal Component Analysis
description	It is difficult to accurately measure parameters by using the traditional soft sensor algorithm when the working condition of industrial process is changed. Therefore, a transfer learning strategy is introduced based on geodesic flow kernel to solve this problem. At the same time, the method is optimized to solve the problems of dynamic characteristic extraction and incomplete Gaussian distribution in industrial process. The augmented matrix is first constructed to deal with the dynamic characteristics of the process. Independent component analysis and principal component analysis are performed on the processed data to extract the non-Gaussian and Gaussian information of the source domain and the target domain. Then, the non-Gaussian and Gaussian information of the source domain is adapted to the target domain respectively on the Grassmann manifold. Finally, the maximum mean discrepancy is used to measure the distribution between the source domain and the target domain after domain adaptation, and the calculated results are applied to construct the weight of the model based on the source domain after domain adaptation. The results show that the method not only reduces the difference of distribution between the source domain and the target domain, but also solves the problems of dynamic characteristic extraction and incomplete Gaussian distribution in industrial process. The validity and the practicability of the model are proved by experiments on Tennessee Eastman data.
format	article
author	LAI Yanbo YAN Gaowei CHENG Lan CHEN Zehua
author_facet	LAI Yanbo YAN Gaowei CHENG Lan CHEN Zehua
author_sort	LAI Yanbo
title	Unsupervised Regression Model of Geodesic Flow Kernel Based on Dynamic Independent Component Analysis and Dynamic Principal Component Analysis
title_short	Unsupervised Regression Model of Geodesic Flow Kernel Based on Dynamic Independent Component Analysis and Dynamic Principal Component Analysis
title_full	Unsupervised Regression Model of Geodesic Flow Kernel Based on Dynamic Independent Component Analysis and Dynamic Principal Component Analysis
title_fullStr	Unsupervised Regression Model of Geodesic Flow Kernel Based on Dynamic Independent Component Analysis and Dynamic Principal Component Analysis
title_full_unstemmed	Unsupervised Regression Model of Geodesic Flow Kernel Based on Dynamic Independent Component Analysis and Dynamic Principal Component Analysis
title_sort	unsupervised regression model of geodesic flow kernel based on dynamic independent component analysis and dynamic principal component analysis
publisher	Editorial Office of Journal of Shanghai Jiao Tong University
publishDate	2020
url	https://doaj.org/article/a55168d826a44c0ab572a2137dbea08a
work_keys_str_mv	AT laiyanbo unsupervisedregressionmodelofgeodesicflowkernelbasedondynamicindependentcomponentanalysisanddynamicprincipalcomponentanalysis AT yangaowei unsupervisedregressionmodelofgeodesicflowkernelbasedondynamicindependentcomponentanalysisanddynamicprincipalcomponentanalysis AT chenglan unsupervisedregressionmodelofgeodesicflowkernelbasedondynamicindependentcomponentanalysisanddynamicprincipalcomponentanalysis AT chenzehua unsupervisedregressionmodelofgeodesicflowkernelbasedondynamicindependentcomponentanalysisanddynamicprincipalcomponentanalysis
_version_	1718444981308358656

Unsupervised Regression Model of Geodesic Flow Kernel Based on Dynamic Independent Component Analysis and Dynamic Principal Component Analysis

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