Orthogonal long short-term memory autoencoder for semi-supervised soft sensor modeling

Data-driven soft sensor methods are popularly applied to predict hard-to-measure variables in industrial production processes. However, in practice, the number of labeled samples is limited, which will affect the accuracy of developed soft sensors. Aiming at this point, semi-supervised soft sensor m...

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Vydané v:	Chemometrics and intelligent laboratory systems Ročník 265; s. 105499
Hlavní autori:	Ma, Fangyuan, Ji, Cheng, Wang, Jingde, Sun, Wei, Romagnoli, Jose A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 15.10.2025
Predmet:	Data-driven method Dynamic feature extraction Feature selection Machine learning Orthogonal auto encoder Feature selection Dynamic feature extraction Machine learning Orthogonal auto encoder Data-driven method
ISSN:	0169-7439
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