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Semi-Supervised Process Data Regression and Application Based on Latent Factor Analysis Model
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DOI：10.1109/TIM.2023.3317484.png)
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Abstract
En 中文
This article investigates the problem of modeling regression with labeled and unlabeled data samples commonly found in industrial processes. By incorporating additional information on unlabeled data samples, a new semi-supervised latent factor analysis model is developed. Compared with the purely supervised regression models which can only use the information of labeled dataset, the semi-supervised model can efficiently extract useful information for improvement of the regression performance. Furthermore, the proposed basic semi-supervised model has been extended to a mixture form, which is capable of describing data from more complex processes. For predictive modeling for key/mass variables, two soft sensors are constructed based on the semi-supervised models. Then, three case studies are used to evaluate the performance of the proposed soft sensing methods.
Keywords:
Data models
Analytical models
Soft sensors
Probabilistic logic
Load modeling
Deep learning
Adaptation models
Expectation-maximization
latent factor analysis
regression modeling
semi-supervised data
soft sensor
Journal
IF:
5.9
Papers:
2.0W
Citations:
5.8W
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