文献类型：期刊文献

英文题名：A semi-supervised soft sensor method based on vine copula regression and tri-training algorithm for complex chemical processes

作者：Liu, Shisong[1];Li, Shaojun[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2022

卷号：120

起止页码：115

外文期刊名：JOURNAL OF PROCESS CONTROL

收录：;EI(收录号：20224813168554);WOS:【SCI-EXPANDED(收录号:WOS:000978866300007)】；

基金：The authors of this paper appreciate the support from the National Natural Science Foundation of China (under Project No. 21676086).

语种：英文

外文关键词：Soft sensor; Tri-training algorithm; Vine copula; Probability model

摘要：Soft sensor technology is an important solution for timely prediction of some difficult-to-measure variables in the chemical process. For the data-driven soft sensor models, the modeling process needs a certain amount of labeled samples. In some actual processes, the number of unlabeled samples is very large, but the labeling process of unlabeled samples is very time-consuming and laborious. When soft sensor modeling is performed in the case of sparse labeled samples, the generalization ability of the model is bound to be poor. To address this problem, a semi-supervised soft sensor method based on vine copula regression and tri-training algorithm (tri-training VCR) is proposed in this paper. Semi-supervised learning is to enhance the performance of supervised learning models by using unlabeled samples. The tri-training algorithm is used to expand the labeled samples through the cooperation of three vine copula regression models with different structures and a new method to judge the confidence degree of pseudo-labeled samples, so as to improve the prediction accuracy of the soft sensor model. A numerical example and an industrial example are used to demonstrate the effectiveness of the proposed method. (c) 2022 Elsevier Ltd. All rights reserved.