Research on Modeling of Industrial Soft Sensor Based on Ensemble Learning
Author:
Affiliation:
1. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China
2. Department of College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China
Funder
Natural Science Foundation of Gansu Province
Industrial Support Program Project of Gansu Province
Backbone Fund of Youth Teachers Capability Promotion
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/7361/10516472/10472934.pdf?arnumber=10472934
Reference35 articles.
1. Design of inferential sensors in the process industry: A review of Bayesian methods
2. A deep learning based data driven soft sensor for bioprocesses
3. Process Data Analytics via Probabilistic Latent Variable Models: A Tutorial Review
4. Interval multiple-output soft sensors development with capacity control for wastewater treatment applications: A comparative study
5. Supervised Latent Factor Analysis for Process Data Regression Modeling and Soft Sensor Application
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1. Soft sensor modeling based on masked convolutional transformer block deep residual shrinkage network;Journal of the Taiwan Institute of Chemical Engineers;2024-10
2. Deep learning with local spatiotemporal structure preserving for soft sensor development of complex industrial processes;Applied Soft Computing;2024-10
3. Graph semi-supervised soft sensor modeling based on label propagation algorithm;Measurement Science and Technology;2024-09-06
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