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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Bibliografische Detailangaben

Hauptverfasser:	Dong Li, Daoping Huang, Guangping Yu, Yiqi Liu
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2020-01-01
Schriftenreihe:	IEEE Access
Schlagworte:	Soft-sensors semi-supervised co-training multi-output recursive partial least square (RPLS) long short-term memory recurrent neural network (LSTM)
Online-Zugang:	https://ieeexplore.ieee.org/document/9028186/
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