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EnglishIn 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.
| Titolo: | Improving soft sensors performance in the presence of small datasets by data selection |
| Autori: | |
| Data di pubblicazione: | 2020 |
| 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. |
| Handle: | http://hdl.handle.net/11570/3180529 |
| ISBN: | 978-1-7281-4460-3 |
| Appare nelle tipologie: | 14.d.3 Contributi in extenso in Atti di convegno |
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