Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Fink, Olga | - |
| dc.contributor.author | Zio, Enrico | - |
| dc.contributor.author | Weidmann, Ulrich | - |
| dc.date.accessioned | 2018-12-17T08:20:31Z | - |
| dc.date.available | 2018-12-17T08:20:31Z | - |
| dc.date.issued | 2015 | - |
| dc.identifier.issn | 0973-1318 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/13894 | - |
| dc.description.abstract | In this paper, we propose to apply deep belief networks (DBN) to predict potential operational disruptions caused by rail vehicle door systems. DBN are a powerful algorithm that is able to detect and extract complex patterns and features in data and has demonstrated superior performance on several benchmark studies. A case study is shown whereby the DBN are trained and applied on real case study from a railway vehicle fleet. The DBN were shown to outperform a feedforward neural network trained by a genetic algorithm. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | RAMS Consultants | de_CH |
| dc.relation.ispartof | International Journal of Performability Engineering | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject.ddc | 004: Informatik | de_CH |
| dc.subject.ddc | 620: Ingenieurwesen | de_CH |
| dc.title | Development and application of deep belief networks for predicting railway operations disruptions | de_CH |
| dc.type | Beitrag in wissenschaftlicher Zeitschrift | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Datenanalyse und Prozessdesign (IDP) | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.issue | 2 | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.pages.end | 134 | de_CH |
| zhaw.pages.start | 121 | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.volume | 11 | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
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Fink, O., Zio, E., & Weidmann, U. (2015). Development and application of deep belief networks for predicting railway operations disruptions. International Journal of Performability Engineering, 11(2), 121–134.
Fink, O., Zio, E. and Weidmann, U. (2015) ‘Development and application of deep belief networks for predicting railway operations disruptions’, International Journal of Performability Engineering, 11(2), pp. 121–134.
O. Fink, E. Zio, and U. Weidmann, “Development and application of deep belief networks for predicting railway operations disruptions,” International Journal of Performability Engineering, vol. 11, no. 2, pp. 121–134, 2015.
FINK, Olga, Enrico ZIO und Ulrich WEIDMANN, 2015. Development and application of deep belief networks for predicting railway operations disruptions. International Journal of Performability Engineering. 2015. Bd. 11, Nr. 2, S. 121–134
Fink, Olga, Enrico Zio, and Ulrich Weidmann. 2015. “Development and Application of Deep Belief Networks for Predicting Railway Operations Disruptions.” International Journal of Performability Engineering 11 (2): 121–34.
Fink, Olga, et al. “Development and Application of Deep Belief Networks for Predicting Railway Operations Disruptions.” International Journal of Performability Engineering, vol. 11, no. 2, 2015, pp. 121–34.
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