|Publication type:||Article in scientific journal|
|Type of review:||Peer review (publication)|
|Title:||Development and application of deep belief networks for predicting railway operations disruptions|
|Authors :||Fink, Olga|
|Published in :||International Journal of Performability Engineering|
|Publisher / Ed. Institution :||RAMS Consultants|
|Subject (DDC) :||004: Computer science |
|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.|
|Fulltext version :||Published version|
|License (according to publishing contract) :||Licence according to publishing contract|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Data Analysis and Process Design (IDP)|
|Appears in Collections:||Publikationen School of Engineering|
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