[1]HE Luosuyang,XIONG Weili.Semi-supervised soft sensor modeling method under the help-training framework[J].CAAI Transactions on Intelligent Systems,2023,18(2):231-239.[doi:10.11992/tis.202111019]
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Semi-supervised soft sensor modeling method under the help-training framework

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 2 Page number: 231-239 Column: 学术论文—机器学习 Public date: 2023-05-05
Title:
Semi-supervised soft sensor modeling method under the help-training framework
Author(s):
HE Luosuyang1;  XIONG Weili1; 2
1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
2. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
Keywords:
soft sensor modeling; semi-supervised; help-training; twin support vector regression; K-nearest neighbor; confidence; learner; debutanizer
CLC:
TP274
DOI:
10.11992/tis.202111019
Abstract:
A semi-supervised twin support vector regression soft sensor modeling method under the help-training framework is proposed to maximize a large number of unlabeled sample information in industrial processes and reduce the impact of process uncertainties on the quality of unlabeled samples. The twin support vector regression is used to build the main learner and add pseudo labels to the unlabeled samples with the highest confidence. Simultaneously, the auxiliary learner is constructed on the basis of the K-nearest neighbor algorithm to maximize the root mean square error of the learner on the nearest neighbor sample set. The candidate sample set screened by this index contains additional data information. The main and auxiliary learners complement each other, which improves the generalization of the model to a certain extent. The prediction model is then obtained by using the help-training framework to improve the sample utilization to mine the unlabeled sample information. Results show that the model has good prediction performance based on the modeling and simulation of the real data in the industrial process of debutanizer.
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