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
Zio, Enrico
Weidmann, Ulrich
Published in: International Journal of Performability Engineering
Volume(Issue): 11
Issue: 2
Pages: 121
Pages to: 134
Issue Date: 2015
Publisher / Ed. Institution: RAMS Consultants
ISSN: 0973-1318
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.
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

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.