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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F04%3APU41333%21RIV06-GA0-26220___
rdf:type
n13:Vysledek skos:Concept
dcterms:description
Application of an adaptive semi-inversion neural controller for a laboratory hot-air system model is described. The system can be used to control two parameters - the airflow and the temperature inside the tunnel. The hot-air system displays negative effects commonly occurring in industrial applications - different static amplifications at different operating points, large offset increasing with time, dead zone and noise. The used semi-inversion neural controller is based on an inversion controller, bbut is capable of solving problems such as oscillating control action, noise sensitivity and ill-estimated parameters in the initial phase of control or adjustment. Pro adaptivní regulátor je nejčastěji používanou identifikační metodou metoda nejmenších čtverců. Tato metoda vyžaduje použití relativně dlouhé periody vzorkování a poruchové signály působící na proces jsou tak hůře vyregulovány. Použití identifikačních metod na bázi neuronových sítí dovoluje podstatným způsobem zkrátit periodu vzorkování. Application of an adaptive semi-inversion neural controller for a laboratory hot-air system model is described. The system can be used to control two parameters - the airflow and the temperature inside the tunnel. The hot-air system displays negative effects commonly occurring in industrial applications - different static amplifications at different operating points, large offset increasing with time, dead zone and noise. The used semi-inversion neural controller is based on an inversion controller, bbut is capable of solving problems such as oscillating control action, noise sensitivity and ill-estimated parameters in the initial phase of control or adjustment.
dcterms:title
Použití neuronových sítí pro řízení horkovzdušného systému Application of Neural Networks for Hot-Air System Control Application of Neural Networks for Hot-Air System Control
skos:prefLabel
Použití neuronových sítí pro řízení horkovzdušného systému Application of Neural Networks for Hot-Air System Control Application of Neural Networks for Hot-Air System Control
skos:notation
RIV/00216305:26220/04:PU41333!RIV06-GA0-26220___
n4:strany
757-760
n4:aktivita
n12:Z n12:P
n4:aktivity
P(GD102/03/H116), Z(MSM 260000013)
n4:cisloPeriodika
2
n4:dodaniDat
n17:2006
n4:domaciTvurceVysledku
n14:5109965 n14:9304010
n4:druhVysledku
n16:J
n4:duvernostUdaju
n9:S
n4:entitaPredkladatele
n15:predkladatel
n4:idSjednocenehoVysledku
555142
n4:idVysledku
RIV/00216305:26220/04:PU41333
n4:jazykVysledku
n19:eng
n4:klicovaSlova
Semi-inversion controller, hot-air system, noise rejection, programmable logic controller, PID
n4:klicoveSlovo
n10:noise%20rejection n10:hot-air%20system n10:Semi-inversion%20controller n10:PID n10:programmable%20logic%20controller
n4:kodStatuVydavatele
US - Spojené státy americké
n4:kontrolniKodProRIV
[6835653942CA]
n4:nazevZdroje
WSEAS Transactions on Systems
n4:obor
n8:BC
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:projekt
n6:GD102%2F03%2FH116
n4:rokUplatneniVysledku
n17:2004
n4:svazekPeriodika
3
n4:tvurceVysledku
Veleba, Václav Pivoňka, Petr
n4:zamer
n5:MSM%20260000013
s:issn
1109-2777
s:numberOfPages
4
n18:organizacniJednotka
26220