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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F05%3APU50366%21RIV06-MSM-26220___
rdf:type
n10:Vysledek skos:Concept
dcterms:description
Článek se zabývá třemi identifikačními metodami estimace parametrů dynamických systémů s ohledem na použití krátké periody vzorkování. In this paper ability of three identification methods to parameter estimation of the dynamic plant with great ratio of its time constant to sampling periods is compared. 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. Taking advantage of this result, we propose here an adaptive conttroller with a neural network as on-line estimator. Simple heuristic synthesis based on modified Ziegler-Nichols open loop method (Z-N 1) are discussed, that deals with bad-estimated model of a plant and gives numerically stable parameters of the PID discrete controller. In this paper ability of three identification methods to parameter estimation of the dynamic plant with great ratio of its time constant to sampling periods is compared. 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. Taking advantage of this result, we propose here an adaptive conttroller with a neural network as on-line estimator. Simple heuristic synthesis based on modified Ziegler-Nichols open loop method (Z-N 1) are discussed, that deals with bad-estimated model of a plant and gives numerically stable parameters of the PID discrete controller.
dcterms:title
Adaptive Controller with Identification Based on Neural Network for Systems with Rapid Sampling Rates Adaptive Controller with Identification Based on Neural Network for Systems with Rapid Sampling Rates Adaptivní regulátor s identifikací neuronovou sítí pro systémy s krátkou periodou vzorkování
skos:prefLabel
Adaptive Controller with Identification Based on Neural Network for Systems with Rapid Sampling Rates Adaptivní regulátor s identifikací neuronovou sítí pro systémy s krátkou periodou vzorkování Adaptive Controller with Identification Based on Neural Network for Systems with Rapid Sampling Rates
skos:notation
RIV/00216305:26220/05:PU50366!RIV06-MSM-26220___
n4:strany
385-388
n4:aktivita
n7:Z
n4:aktivity
Z(MSM0021630503)
n4:cisloPeriodika
4
n4:dodaniDat
n16:2006
n4:domaciTvurceVysledku
n6:5109965 n6:9304010
n4:druhVysledku
n13:J
n4:duvernostUdaju
n9:S
n4:entitaPredkladatele
n17:predkladatel
n4:idSjednocenehoVysledku
511376
n4:idVysledku
RIV/00216305:26220/05:PU50366
n4:jazykVysledku
n18:eng
n4:klicovaSlova
Rapid Sampling, Quantization, Neural Network, Training Set, Levenberg-Marquardt Minimization, Discrete PID Controller, RLS Identification Method
n4:klicoveSlovo
n11:Levenberg-Marquardt%20Minimization n11:Discrete%20PID%20Controller n11:Quantization n11:Training%20Set n11:RLS%20Identification%20Method n11:Neural%20Network n11:Rapid%20Sampling
n4:kodStatuVydavatele
US - Spojené státy americké
n4:kontrolniKodProRIV
[BDB68AE14E69]
n4:nazevZdroje
WSEAS Transactions on Systems
n4:obor
n14:BC
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:rokUplatneniVysledku
n16:2005
n4:svazekPeriodika
4
n4:tvurceVysledku
Pivoňka, Petr Veleba, Václav
n4:zamer
n5:MSM0021630503
s:issn
1109-2777
s:numberOfPages
4
n15:organizacniJednotka
26220