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Statements

Subject Item
n2:RIV%2F68407700%3A21220%2F10%3A00170332%21RIV14-MSM-21220___
rdf:type
skos:Concept n17:Vysledek
rdfs:seeAlso
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5596614
dcterms:description
The paper discusses results and advantages of the application of quadratic neural units and novel quadratic neural network to modeling of real data for purposes of validation of measured data in energetic processes. A feed-forward network of quadratic neural units (a class of higher order neural network) with sequential learning is presented. This quadratic network with this learning technique reduces computational time for models with large number of inputs, sustains optimization convexity of a quadratic model, and also displays sufficient non-linear approximation capability for the real process. A comparison of performances of the quadratic neural units, quadratic neural networks, and the use of common multilayer feed-forward neural networks all trained by Levenberg-Marquardt algorithm is discussed. The paper discusses results and advantages of the application of quadratic neural units and novel quadratic neural network to modeling of real data for purposes of validation of measured data in energetic processes. A feed-forward network of quadratic neural units (a class of higher order neural network) with sequential learning is presented. This quadratic network with this learning technique reduces computational time for models with large number of inputs, sustains optimization convexity of a quadratic model, and also displays sufficient non-linear approximation capability for the real process. A comparison of performances of the quadratic neural units, quadratic neural networks, and the use of common multilayer feed-forward neural networks all trained by Levenberg-Marquardt algorithm is discussed.
dcterms:title
Quadratic Neural Unit and its Network in Validation of Process Data of Steam Turbine Loop and Energetic Boiler Quadratic Neural Unit and its Network in Validation of Process Data of Steam Turbine Loop and Energetic Boiler
skos:prefLabel
Quadratic Neural Unit and its Network in Validation of Process Data of Steam Turbine Loop and Energetic Boiler Quadratic Neural Unit and its Network in Validation of Process Data of Steam Turbine Loop and Energetic Boiler
skos:notation
RIV/68407700:21220/10:00170332!RIV14-MSM-21220___
n3:aktivita
n8:P
n3:aktivity
P(2B06023)
n3:dodaniDat
n12:2014
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n4:D
n3:duvernostUdaju
n14:S
n3:entitaPredkladatele
n23:predkladatel
n3:idSjednocenehoVysledku
283566
n3:idVysledku
RIV/68407700:21220/10:00170332
n3:jazykVysledku
n21:eng
n3:klicovaSlova
Quadratic Neural Unit; Validation of Data; Energetic Processes; Levenberg-Marquardt Training Algorithm; Approximation of Non-linear Process
n3:klicoveSlovo
n5:Quadratic%20Neural%20Unit n5:Levenberg-Marquardt%20Training%20Algorithm n5:Approximation%20of%20Non-linear%20Process n5:Energetic%20Processes n5:Validation%20of%20Data
n3:kontrolniKodProRIV
[A1081B4A9C30]
n3:mistoKonaniAkce
Barcelona
n3:mistoVydani
Piscataway
n3:nazevZdroje
2010 IEEE World Congress on Computational Inteligence/ International Joint Conference on Neural Networks 2010
n3:obor
n16:BC
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n18:2B06023
n3:rokUplatneniVysledku
n12:2010
n3:tvurceVysledku
Lepold, Martin Bukovský, Ivo Bíla, Jiří
n3:typAkce
n6:WRD
n3:wos
000287421403081
n3:zahajeniAkce
2010-07-18+02:00
s:issn
1098-7576
s:numberOfPages
7
n22:doi
10.1109/IJCNN.2010.5596614
n19:hasPublisher
IEEE
n13:isbn
978-1-4244-6917-8
n11:organizacniJednotka
21220