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Statements

Subject Item
n2:RIV%2F00216305%3A26210%2F00%3A00000141%21RIV%2F2001%2FMSM%2F262101%2FN
rdf:type
skos:Concept n17:Vysledek
dcterms:description
The paper compaares feed-forward and recurrent neural network architectures use for identification of simple non-linear dynamic system in the terms of generalization abilities, result precision and computational requirements. Comparison has been made on a number of dynamic systems using Elman model as a representative of recurrent networks and various verions of gradient training applied on layered feed-forward networks with one hidden layer. The paper compaares feed-forward and recurrent neural network architectures use for identification of simple non-linear dynamic system in the terms of generalization abilities, result precision and computational requirements. Comparison has been made on a number of dynamic systems using Elman model as a representative of recurrent networks and various verions of gradient training applied on layered feed-forward networks with one hidden layer.
dcterms:title
Recurrent versus feed-forward neural networks used for identification of non-linear dynamic systems Recurrent versus feed-forward neural networks used for identification of non-linear dynamic systems
skos:prefLabel
Recurrent versus feed-forward neural networks used for identification of non-linear dynamic systems Recurrent versus feed-forward neural networks used for identification of non-linear dynamic systems
skos:notation
RIV/00216305:26210/00:00000141!RIV/2001/MSM/262101/N
n3:strany
109
n3:aktivita
n11:P n11:Z
n3:aktivity
P(VS96122), Z(MSM 262100001)
n3:dodaniDat
n6:2001
n3:domaciTvurceVysledku
Březina, Tomáš Krejsa, Jiří Kratochvíl, Ctirad
n3:druhVysledku
n20:D
n3:duvernostUdaju
n14:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
724792
n3:idVysledku
RIV/00216305:26210/00:00000141
n3:jazykVysledku
n18:eng
n3:klicovaSlova
neural networks;non-linear dynamic system;identification
n3:klicoveSlovo
n5:non-linear%20dynamic%20system n5:identification n5:neural%20networks
n3:kontrolniKodProRIV
[748E26A7D376]
n3:mistoVydani
Warsaw, Poland
n3:nazevZdroje
Mechatronics 2000
n3:obor
n13:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n12:VS96122
n3:rokUplatneniVysledku
n6:2000
n3:tvurceVysledku
Krejsa, Jiří Březina, Tomáš Kratochvíl, Ctirad
n3:zamer
n4:MSM%20262100001
s:numberOfPages
4
n16:hasPublisher
MEANDER S.C.
n8:isbn
83-914366-0-8
n9:organizacniJednotka
26210