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Statements

Subject Item
n2:RIV%2F68407700%3A21220%2F10%3A00173684%21RIV14-MSM-21220___
rdf:type
n17:Vysledek skos:Concept
rdfs:seeAlso
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5599677
dcterms:description
The paper discusses the quadratic neural unit (QNU) and highlights its attractiveness for industrial applications such as for plant modeling, control, and time series prediction. Linear systems are still often preferred in control applications for their solvable and single solution nature and for the clarity to the most application engineers. Artificial neural networks are powerful cognitive nonlinear tools, but their nonlinear strength is naturally repaid with the local minima problem, overfitting, and high demands for application-correct neural architecture and optimization technique that often require skilled users. The QNU is the important midpoint between linear systems and highly nonlinear neural networks because the QNU is relatively very strong in nonlinear approximation; however, its optimization and performance have fast and convex-like nature, and its mathematical structure and the derivation of the learning rules is very comprehensible and efficient for implementation. The paper discusses the quadratic neural unit (QNU) and highlights its attractiveness for industrial applications such as for plant modeling, control, and time series prediction. Linear systems are still often preferred in control applications for their solvable and single solution nature and for the clarity to the most application engineers. Artificial neural networks are powerful cognitive nonlinear tools, but their nonlinear strength is naturally repaid with the local minima problem, overfitting, and high demands for application-correct neural architecture and optimization technique that often require skilled users. The QNU is the important midpoint between linear systems and highly nonlinear neural networks because the QNU is relatively very strong in nonlinear approximation; however, its optimization and performance have fast and convex-like nature, and its mathematical structure and the derivation of the learning rules is very comprehensible and efficient for implementation.
dcterms:title
Quadratic neural unit is a good compromise between linear models and neural networks for industrial applications Quadratic neural unit is a good compromise between linear models and neural networks for industrial applications
skos:prefLabel
Quadratic neural unit is a good compromise between linear models and neural networks for industrial applications Quadratic neural unit is a good compromise between linear models and neural networks for industrial applications
skos:notation
RIV/68407700:21220/10:00173684!RIV14-MSM-21220___
n4:aktivita
n22:S
n4:aktivity
S
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n9:2014
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n11:6347118 n11:9629300 n11:8200599 n11:8985952
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n7:D
n4:duvernostUdaju
n15:S
n4:entitaPredkladatele
n21:predkladatel
n4:idSjednocenehoVysledku
283567
n4:idVysledku
RIV/68407700:21220/10:00173684
n4:jazykVysledku
n6:eng
n4:klicovaSlova
cognitive systems; industrial control; nonlinear control systems; neurocontrollers; quadratic neural unit
n4:klicoveSlovo
n5:cognitive%20systems n5:industrial%20control n5:quadratic%20neural%20unit n5:neurocontrollers n5:nonlinear%20control%20systems
n4:kontrolniKodProRIV
[F85977E48EE4]
n4:mistoKonaniAkce
Beijing
n4:mistoVydani
Los Alamitos
n4:nazevZdroje
Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on
n4:obor
n20:BC
n4:pocetDomacichTvurcuVysledku
4
n4:pocetTvurcuVysledku
6
n4:rokUplatneniVysledku
n9:2010
n4:tvurceVysledku
Rodriguez, Ricardo Bukovský, Ivo Homma, N. Mironovová, Martina Smetana, Ladislav Vrána, Stanislav
n4:typAkce
n10:WRD
n4:zahajeniAkce
2010-07-07+02:00
s:numberOfPages
5
n19:doi
10.1109/COGINF.2010.5599677
n14:hasPublisher
IEEE Computer Society Press
n16:isbn
978-1-4244-8040-1
n18:organizacniJednotka
21220