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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F01%3APU21008%21RIV%2F2002%2FGA0%2F262202%2FN
rdf:type
n17:Vysledek skos:Concept
dcterms:description
This paper deals with the use of artificial neural networks employed as an on-line trained controller for a real process and simulation model control. Well-known back-propagation method is used as a learning algorithm intended to minimize the difference between the plant’s actual response and the desired reference signal. The influence of neural network’s parameters on a controlled plant output is discussed. We also attempted to find the rules of these parameters adjustment in view of the type of a trannsfer function in Laplace transform and tested the robustness of our controller burdened with the error signal. Some simulation and real process control results are also presented to evaluate the proposed design. Discussed in the last chapter are the possibilities of creating an adaptive neural controller. This paper deals with the use of artificial neural networks employed as an on-line trained controller for a real process and simulation model control. Well-known back-propagation method is used as a learning algorithm intended to minimize the difference between the plant’s actual response and the desired reference signal. The influence of neural network’s parameters on a controlled plant output is discussed. We also attempted to find the rules of these parameters adjustment in view of the type of a trannsfer function in Laplace transform and tested the robustness of our controller burdened with the error signal. Some simulation and real process control results are also presented to evaluate the proposed design. Discussed in the last chapter are the possibilities of creating an adaptive neural controller.
dcterms:title
Artificial Neural Networks for On-Line Trained Controllers Artificial Neural Networks for On-Line Trained Controllers
skos:prefLabel
Artificial Neural Networks for On-Line Trained Controllers Artificial Neural Networks for On-Line Trained Controllers
skos:notation
RIV/00216305:26220/01:PU21008!RIV/2002/GA0/262202/N
n3:strany
189-194
n3:aktivita
n16:Z n16:P
n3:aktivity
P(GA102/01/1485), Z(MSM 260000013)
n3:dodaniDat
n14:2002
n3:domaciTvurceVysledku
n19:9304010
n3:druhVysledku
n10:C
n3:duvernostUdaju
n15:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
673888
n3:idVysledku
RIV/00216305:26220/01:PU21008
n3:jazykVysledku
n7:eng
n3:klicovaSlova
back-propagation, artificial neural nets, neural controller, adaptive neural controller
n3:klicoveSlovo
n9:back-propagation n9:adaptive%20neural%20controller n9:neural%20controller n9:artificial%20neural%20nets
n3:kontrolniKodProRIV
[F4A070546556]
n3:mistoVydani
http://www.worldses.org
n3:nazevEdiceCisloSvazku
Electrical and Computer Engineering Series - A se
n3:nazevZdroje
Advances in Systems Science: Measurement, Circuits and Control
n3:obor
n18:JB
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:pocetUcastnikuAkce
0
n3:pocetZahranicnichUcastnikuAkce
0
n3:projekt
n20:GA102%2F01%2F1485
n3:rokUplatneniVysledku
n14:2001
n3:tvurceVysledku
Pivoňka, Petr
n3:zamer
n12:MSM%20260000013
s:numberOfPages
6
n8:hasPublisher
Published by WSES Press, http://www.worldses.org
n5:isbn
960-8052-39-4
n21:organizacniJednotka
26220