A new strategy to improve generalization capabiliy of data-driven soft sensor for industrial processes is proposed in the paper. The method is useful when a limited data set is available. The proposed approach is based on the integration of bootstrap resampling, noise injection and stacked neural networks. In the paper it has been applied to develop a Soft Sensor for the estimation of the Freezing Point of Kerosene in an atmospheric distillation unit (Topping) working in a refinery in Sicily, Italy. © 2009 IFAC.
Improving generalization in Neural Soft Sensor design
Napoli, Giuseppe
;
2009
Abstract
A new strategy to improve generalization capabiliy of data-driven soft sensor for industrial processes is proposed in the paper. The method is useful when a limited data set is available. The proposed approach is based on the integration of bootstrap resampling, noise injection and stacked neural networks. In the paper it has been applied to develop a Soft Sensor for the estimation of the Freezing Point of Kerosene in an atmospheric distillation unit (Topping) working in a refinery in Sicily, Italy. © 2009 IFAC.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
1-s2.0-S1474667016388498-main.pdf
solo utenti autorizzati
Tipologia:
Versione Editoriale (PDF)
Licenza:
NON PUBBLICO - Accesso privato/ristretto
Dimensione
485.3 kB
Formato
Adobe PDF
|
485.3 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
