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
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;
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
DOI :
10.1109/MED.2008.4602160
