In the paper, a method for improving the performance of data-driven Soft Sensors in the presence of small datasets problems is proposed. The method is based on a preliminary feature extraction phase, followed by a data selection method. Selected data are used to train neural-network-based Soft Sensors, modeling the featureoutput nonlinear relationship. A case of study referring to a Sour Water Stripping plant is used in the paper to validate the suitability of the proposed methodology.
Improving soft sensors performance in the presence of small datasets by data selection
Graziani S.
;Xibilia M. G.
2020-01-01
Abstract
In the paper, a method for improving the performance of data-driven Soft Sensors in the presence of small datasets problems is proposed. The method is based on a preliminary feature extraction phase, followed by a data selection method. Selected data are used to train neural-network-based Soft Sensors, modeling the featureoutput nonlinear relationship. A case of study referring to a Sour Water Stripping plant is used in the paper to validate the suitability of the proposed methodology.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
