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Statements

Subject Item
n2:RIV%2F70883521%3A28110%2F01%3A00000044%21RIV%2F2002%2FGA0%2F281102%2FN
rdf:type
skos:Concept n17:Vysledek
dcterms:description
Predictive control using artificial neural network with algorithm backpropagation is presented in the paper. This is the %22learning teacher%22 type of the ANN (artifiicial neural network). Pairs of corresponding values to each other are put at the inputs and outputs in the training phase. Then, the information %22overflows%22 from inputs to outputs and back until it gains the minimal global error per the number of epochs. After teaching, the ANN is able to predict the output value perfectly although the disturbance signal is presented. Neural network is by this way used as nonlinear substitution for very complex systems and controlled output value is predicted. This value is then used for PSD controller adaptive control. The results are presented as simulatedexamples in the environment of MATLAB and Simulink programs. Predictive control using artificial neural network with algorithm backpropagation is presented in the paper. This is the %22learning teacher%22 type of the ANN (artifiicial neural network). Pairs of corresponding values to each other are put at the inputs and outputs in the training phase. Then, the information %22overflows%22 from inputs to outputs and back until it gains the minimal global error per the number of epochs. After teaching, the ANN is able to predict the output value perfectly although the disturbance signal is presented. Neural network is by this way used as nonlinear substitution for very complex systems and controlled output value is predicted. This value is then used for PSD controller adaptive control. The results are presented as simulatedexamples in the environment of MATLAB and Simulink programs.
dcterms:title
Prediction and predictive control using feedforward neural network Prediction and predictive control using feedforward neural network
skos:prefLabel
Prediction and predictive control using feedforward neural network Prediction and predictive control using feedforward neural network
skos:notation
RIV/70883521:28110/01:00000044!RIV/2002/GA0/281102/N
n3:strany
1;6
n3:aktivita
n10:Z n10:P
n3:aktivity
P(GA102/00/0526), P(GA102/99/1292), Z(MSM 281100001)
n3:dodaniDat
n4:2002
n3:domaciTvurceVysledku
n11:1846787
n3:druhVysledku
n20:D
n3:duvernostUdaju
n13:S
n3:entitaPredkladatele
n21:predkladatel
n3:idSjednocenehoVysledku
692368
n3:idVysledku
RIV/70883521:28110/01:00000044
n3:jazykVysledku
n22:eng
n3:klicovaSlova
predictive control, neural networks, predictor, PSD controller
n3:klicoveSlovo
n5:PSD%20controller n5:neural%20networks n5:predictor n5:predictive%20control
n3:kontrolniKodProRIV
[C62193B9020B]
n3:mistoKonaniAkce
Zlín
n3:mistoVydani
Zlín
n3:nazevZdroje
Proc. 4th International Conference on Prediction and Nonlinear Dynamics
n3:obor
n6:BC
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
2
n3:pocetUcastnikuAkce
0
n3:pocetZahranicnichUcastnikuAkce
0
n3:projekt
n7:GA102%2F99%2F1292 n7:GA102%2F00%2F0526
n3:rokUplatneniVysledku
n4:2001
n3:tvurceVysledku
Bobál, Vladimír Janota, David
n3:typAkce
n16:CST
n3:zahajeniAkce
2001-09-25+02:00
n3:zamer
n15:MSM%20281100001
s:numberOfPages
6
n19:hasPublisher
Univerzita Tomáše Bati ve Zlíně
n12:isbn
80-7318-030-8
n18:organizacniJednotka
28110