Title: Development and application of deep belief networks for predicting railway operations disruptions
Authors : Fink, Olga
Zio, Enrico
Weidmann, Ulrich
Published in : International journal of performability engineering
Volume(Issue) : 11
Issue : 2
Pages : 121
Pages to: 134
Publisher / Ed. Institution : RAMS Consultants
Issue Date: 2015
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (Publication)
Language : English
Subject (DDC) : 004: Computer science
620: Engineering
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.
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Publication type: Article in scientific Journal
ISSN: 0973-1318
URI: https://digitalcollection.zhaw.ch/handle/11475/13894
Appears in Collections:Publikationen School of Engineering

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