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Statements

Subject Item
n2:RIV%2F68407700%3A21220%2F14%3A00226709%21RIV15-MSM-21220___
rdf:type
skos:Concept n16:Vysledek
rdfs:seeAlso
http://airccj.org/CSCP/vol4/csit41928.pdf
dcterms:description
The paper presents a study of an adaptive approach to lateral skew control for an experimental railway stand. The preliminary experiments with the real experimental railway stand and simulations with its 3-D mechanical model, indicates difficulties of model-based control of the device. Thus, use of neural networks for identification and control of lateral skew shall be investigated. This paper focuses on real-data based modelling of the railway stand by various neural network models, i.e; linear neural unit and quadratic neural unit architectures. Furthermore, training methods of these neural architectures as such, real-time-recurrent-learning and a variation of back-propagation-through-time are examined, accompanied by a discussion of the produced experimental results. The paper presents a study of an adaptive approach to lateral skew control for an experimental railway stand. The preliminary experiments with the real experimental railway stand and simulations with its 3-D mechanical model, indicates difficulties of model-based control of the device. Thus, use of neural networks for identification and control of lateral skew shall be investigated. This paper focuses on real-data based modelling of the railway stand by various neural network models, i.e; linear neural unit and quadratic neural unit architectures. Furthermore, training methods of these neural architectures as such, real-time-recurrent-learning and a variation of back-propagation-through-time are examined, accompanied by a discussion of the produced experimental results.
dcterms:title
Neural Network Approach to Railway Stand Lateral Skew Control Neural Network Approach to Railway Stand Lateral Skew Control
skos:prefLabel
Neural Network Approach to Railway Stand Lateral Skew Control Neural Network Approach to Railway Stand Lateral Skew Control
skos:notation
RIV/68407700:21220/14:00226709!RIV15-MSM-21220___
n3:aktivita
n13:S
n3:aktivity
S
n3:dodaniDat
n18:2015
n3:domaciTvurceVysledku
n14:5968224 n14:8200599 n14:4271300 n14:3495981
n3:druhVysledku
n19:D
n3:duvernostUdaju
n10:S
n3:entitaPredkladatele
n8:predkladatel
n3:idSjednocenehoVysledku
32233
n3:idVysledku
RIV/68407700:21220/14:00226709
n3:jazykVysledku
n17:eng
n3:klicovaSlova
Roller Rig; Linear Neural Unit; Quadratic Neural Unit; Real-Time-Recurrent-Learning (RTRL); Back-Propagation-Through-Time (BPTT)
n3:klicoveSlovo
n6:Linear%20Neural%20Unit n6:Back-Propagation-Through-Time%20%28BPTT%29 n6:Real-Time-Recurrent-Learning%20%28RTRL%29 n6:Quadratic%20Neural%20Unit n6:Roller%20Rig
n3:kontrolniKodProRIV
[F3B79E8EB948]
n3:mistoKonaniAkce
Sydney
n3:mistoVydani
Chennai, Tamil Nadu
n3:nazevZdroje
Computer Science & Information Technology
n3:obor
n4:BC
n3:pocetDomacichTvurcuVysledku
4
n3:pocetTvurcuVysledku
4
n3:rokUplatneniVysledku
n18:2014
n3:tvurceVysledku
Cejnek, Matouš Bukovský, Ivo Kalivoda, Jan Beneš, Peter Mark
n3:typAkce
n12:WRD
n3:zahajeniAkce
2014-02-21+01:00
s:issn
2231-5403
s:numberOfPages
13
n15:doi
10.5121/csit.2014.4228
n20:hasPublisher
AIRCC Publishing Corporation
n9:organizacniJednotka
21220