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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F05%3APU50365%21RIV06-MSM-26220___
rdf:type
n17:Vysledek skos:Concept
dcterms:description
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. Článek porovnává tři identifikační metody pro odhad parametrů dynamických systému s ohledem na požadavek krátké periody vzorkování.
dcterms:title
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:prefLabel
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:notation
RIV/00216305:26220/05:PU50365!RIV06-MSM-26220___
n3:strany
1-4
n3:aktivita
n20:Z
n3:aktivity
Z(MSM0021630503)
n3:dodaniDat
n5:2006
n3:domaciTvurceVysledku
n4:5109965 n4:9304010
n3:druhVysledku
n12:D
n3:duvernostUdaju
n18:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
511375
n3:idVysledku
RIV/00216305:26220/05:PU50365
n3:jazykVysledku
n11:eng
n3:klicovaSlova
Rapid Sampling, Quantization, Neural Network, Training Set, Levenberg-Marquardt Minimization, Discrete PID Controller, RLS Identification Method
n3:klicoveSlovo
n7:Neural%20Network n7:RLS%20Identification%20Method n7:Training%20Set n7:Discrete%20PID%20Controller n7:Levenberg-Marquardt%20Minimization n7:Quantization n7:Rapid%20Sampling
n3:kontrolniKodProRIV
[3FA06EA57201]
n3:mistoKonaniAkce
Lisabon
n3:mistoVydani
Lisabon, Portugalsko
n3:nazevZdroje
WSEAS International Conferences NN'05, FS'05,EC'05
n3:obor
n8:BC
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n5:2005
n3:tvurceVysledku
Pivoňka, Petr Veleba, Václav
n3:typAkce
n9:WRD
n3:zahajeniAkce
2005-06-16+02:00
n3:zamer
n13:MSM0021630503
s:numberOfPages
4
n10:hasPublisher
WSEAS
n19:isbn
960-8457-24-6
n15:organizacniJednotka
26220