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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F07%3APU69904%21RIV08-GA0-26220___
rdf:type
skos:Concept n14:Vysledek
dcterms:description
Nový přístup pro identifikaci dynamických systémů s neuronovými sítěmi dovoluje použití krátké periody vzorkování. The new approach to analysis of on-line identification methods based on one-step-ahead prediction clears up their sensitivity to disturbances in control loop and explain why should be neural network based identification better then classical by using of short sampling period. The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. On one hand faster disturbance rejection due to short sampling period can be an advantage but on the other hand it brings us some practical problems. Particularly, quantization error and finite numerical precision of industrial controller must be considered in the real process control. We concentrate our attention on dealing with adverse effects that work on real-time identification of process, especially quantization. It is shown; that a neural network applied to on-line identification process produces more stable solution in the rapid sampling. The new approach to analysis of on-line identification methods based on one-step-ahead prediction clears up their sensitivity to disturbances in control loop and explain why should be neural network based identification better then classical by using of short sampling period. The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. On one hand faster disturbance rejection due to short sampling period can be an advantage but on the other hand it brings us some practical problems. Particularly, quantization error and finite numerical precision of industrial controller must be considered in the real process control. We concentrate our attention on dealing with adverse effects that work on real-time identification of process, especially quantization. It is shown; that a neural network applied to on-line identification process produces more stable solution in the rapid sampling.
dcterms:title
Použití neuronových sítí v adaptivním řízení při krátké periodě vzorkování Using of Neural Network Based Identification for Short Sampling Period in Adaptive Control Using of Neural Network Based Identification for Short Sampling Period in Adaptive Control
skos:prefLabel
Použití neuronových sítí v adaptivním řízení při krátké periodě vzorkování Using of Neural Network Based Identification for Short Sampling Period in Adaptive Control Using of Neural Network Based Identification for Short Sampling Period in Adaptive Control
skos:notation
RIV/00216305:26220/07:PU69904!RIV08-GA0-26220___
n5:strany
217-222
n5:aktivita
n18:P n18:Z
n5:aktivity
P(GA102/06/1132), Z(MSM0021630503)
n5:dodaniDat
n17:2008
n5:domaciTvurceVysledku
n16:9304010 n16:5109965 n16:2160188
n5:druhVysledku
n13:D
n5:duvernostUdaju
n22:S
n5:entitaPredkladatele
n20:predkladatel
n5:idSjednocenehoVysledku
457028
n5:idVysledku
RIV/00216305:26220/07:PU69904
n5:jazykVysledku
n11:eng
n5:klicovaSlova
Rapid sampling domain, Neural networks for identification, Comparison of identifications methods
n5:klicoveSlovo
n12:Rapid%20sampling%20domain n12:Neural%20networks%20for%20identification n12:Comparison%20of%20identifications%20methods
n5:kontrolniKodProRIV
[F9DF93ADA84F]
n5:mistoKonaniAkce
Crete, Greece
n5:mistoVydani
Řecko
n5:nazevZdroje
Systems Theory and Applications
n5:obor
n8:BC
n5:pocetDomacichTvurcuVysledku
3
n5:pocetTvurcuVysledku
3
n5:projekt
n21:GA102%2F06%2F1132
n5:rokUplatneniVysledku
n17:2007
n5:tvurceVysledku
Ošmera, Pavel Pivoňka, Petr Veleba, Václav
n5:typAkce
n19:WRD
n5:zahajeniAkce
2007-07-23+02:00
n5:zamer
n6:MSM0021630503
s:numberOfPages
6
n7:hasPublisher
WSEAS
n15:isbn
978-960-8457-90-4
n9:organizacniJednotka
26220