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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F08%3APU73499%21RIV10-MSM-26220___
rdf:type
n7:Vysledek skos:Concept
dcterms:description
The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. 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. 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 domain. The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. 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. 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 domain.
dcterms:title
Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period
skos:prefLabel
Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period
skos:notation
RIV/00216305:26220/08:PU73499!RIV10-MSM-26220___
n4:aktivita
n9:Z n9:P
n4:aktivity
P(GA102/06/1132), Z(MSM0021630529)
n4:cisloPeriodika
1
n4:dodaniDat
n17:2010
n4:domaciTvurceVysledku
n13:5109965 n13:9304010
n4:druhVysledku
n12:J
n4:duvernostUdaju
n11:S
n4:entitaPredkladatele
n18:predkladatel
n4:idSjednocenehoVysledku
354878
n4:idVysledku
RIV/00216305:26220/08:PU73499
n4:jazykVysledku
n16:eng
n4:klicovaSlova
Adaptive Controllers, Neural Networks for Identification, Comparison of Identifications methods, Rapid Sampling Domain.
n4:klicoveSlovo
n5:Neural%20Networks%20for%20Identification n5:Adaptive%20Controllers n5:Comparison%20of%20Identifications%20methods n5:Rapid%20Sampling%20Domain.
n4:kodStatuVydavatele
US - Spojené státy americké
n4:kontrolniKodProRIV
[7BDCC13B8D7D]
n4:nazevZdroje
INTERNATIONAL JOURNAL of CIRCUITS, SYSTEMS and SIGNAL PROCESSING
n4:obor
n15:BC
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:projekt
n10:GA102%2F06%2F1132
n4:rokUplatneniVysledku
n17:2008
n4:svazekPeriodika
1
n4:tvurceVysledku
Veleba, Václav Pivoňka, Petr
n4:zamer
n19:MSM0021630529
s:issn
1998-0140
s:numberOfPages
6
n14:organizacniJednotka
26220