Learning Adaptive Semi-Supervised Multi-Output Soft-Sensors With Co-Training of Heterogeneous Models

Learning Adaptive Semi-Supervised Multi-Output Soft-Sensors With Co-Training of Heterogeneous Models

Soft-sensors are widely utilized for predictions of important but hard-to-measure variables in industrial processes. However, significant variations, process uncertainties, negative influence of external environment and insufficient use of unlabeled data always cause the attenuation of prediction pe...

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Bibliographic Details
Main Authors: Dong Li, Daoping Huang, Guangping Yu, Yiqi Liu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Soft-sensors
semi-supervised
co-training
multi-output
recursive partial least square (RPLS)
long short-term memory recurrent neural network (LSTM)
Online Access:https://ieeexplore.ieee.org/document/9028186/

Internet

https://ieeexplore.ieee.org/document/9028186/

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