Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling

Latent variable (LV) models provide explicit representations of underlying driving forces of process variations and retain the dominant information of process data. In this study, slow features (SFs) as temporally correlated LVs are derived using probabilistic SF analysis. SFs evolving in a state‐sp...

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Veröffentlicht in:	AIChE journal Jg. 61; H. 12; S. 4126 - 4139
Hauptverfasser:	Shang, Chao, Huang, Biao, Yang, Fan, Huang, Dexian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Blackwell Publishing Ltd 01.12.2015 American Institute of Chemical Engineers
Schlagworte:	Algorithms Chemical engineering Chemical engineers Correlation analysis Data analysis Evolution latent variable model Learning Mathematical models Probabilistic methods Probability Probability theory process data analysis Representations Sensors slow feature analysis soft sensor
ISSN:	0001-1541, 1547-5905
Online-Zugang:	Volltext
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