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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Vydané v:	AIChE journal Ročník 61; číslo 12; s. 4126 - 4139
Hlavní autori:	Shang, Chao, Huang, Biao, Yang, Fan, Huang, Dexian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Blackwell Publishing Ltd 01.12.2015 American Institute of Chemical Engineers
Predmet:	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
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