DocumentCode
2619027
Title
Stacking approaches for the design of soft sensors using small data set
Author
Di Bella, A. ; Graziani, S. ; Napoli, G. ; Xibilia, M.G.
Author_Institution
DIEES, Univ. degli Studi di Catania, Catania
fYear
2008
fDate
25-27 June 2008
Firstpage
1810
Lastpage
1815
Abstract
In this paper a number of approaches to design a soft sensor for an industrial plant in case of small data set are compared. In particular different strategies to aggregate suboptimal models obtained by bootstrapped neural networks and noise injection are considered. An industrial case of study, consisting in the estimation of the T95% of a Thermal Cracking Unit (TCU) of a refinery in Sicily is considered to evaluate the performance of the different approaches.
Keywords
data structures; industrial engineering; neural nets; virtual instrumentation; bootstrapped neural network; industrial plant; noise injection; small data set; soft sensor design; stack approach; Aggregates; Automatic control; Design automation; Industrial plants; Monitoring; Neural networks; Petroleum; Refining; Stacking; Training data; Industrial plants; neural models; small data sets; soft sensors; stacking approaches;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Automation, 2008 16th Mediterranean Conference on
Conference_Location
Ajaccio
Print_ISBN
978-1-4244-2504-4
Electronic_ISBN
978-1-4244-2505-1
Type
conf
DOI
10.1109/MED.2008.4602160
Filename
4602160
Link To Document