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Statements

Subject Item
n2:RIV%2F61989100%3A27360%2F14%3A86092124%21RIV15-MSM-27360___
rdf:type
n11:Vysledek skos:Concept
dcterms:description
The attention is devoted to the active and passive optical fibres of the suitable glasses. Because of high structural sensitivity of optical transmittance to glass composition we present sophisticated solution of experimental data evaluation to obtain way directly predict the proper glass composition-transmitance relation. In the paper we present application of artificial neural network (ANN) on relation between glass composition versus optical transmittance of the chosen glass systems of Sb2O3 - PbCl2 and Sb2O3 – PbO – M2O, where M was Na, K and Li, respectively. The developed neural model predicts optical transmittance with sufficiently small error (7%). Neural networks are able to simulate dependences which can be hardly solved by classic methods of statistic data evaluation and they are able to express more complex relations than these methods. The attention is devoted to the active and passive optical fibres of the suitable glasses. Because of high structural sensitivity of optical transmittance to glass composition we present sophisticated solution of experimental data evaluation to obtain way directly predict the proper glass composition-transmitance relation. In the paper we present application of artificial neural network (ANN) on relation between glass composition versus optical transmittance of the chosen glass systems of Sb2O3 - PbCl2 and Sb2O3 – PbO – M2O, where M was Na, K and Li, respectively. The developed neural model predicts optical transmittance with sufficiently small error (7%). Neural networks are able to simulate dependences which can be hardly solved by classic methods of statistic data evaluation and they are able to express more complex relations than these methods.
dcterms:title
The Neural Network Analysis of Optical Glasses Transmittance The Neural Network Analysis of Optical Glasses Transmittance
skos:prefLabel
The Neural Network Analysis of Optical Glasses Transmittance The Neural Network Analysis of Optical Glasses Transmittance
skos:notation
RIV/61989100:27360/14:86092124!RIV15-MSM-27360___
n3:aktivita
n10:N
n3:aktivity
N
n3:dodaniDat
n17:2015
n3:domaciTvurceVysledku
n4:2407558 n4:9573410 n4:8578834
n3:druhVysledku
n14:D
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
32231
n3:idVysledku
RIV/61989100:27360/14:86092124
n3:jazykVysledku
n13:eng
n3:klicovaSlova
antimonate glasses, structure, neural networks
n3:klicoveSlovo
n5:antimonate%20glasses n5:neural%20networks n5:structure
n3:kontrolniKodProRIV
[3C097C401180]
n3:mistoKonaniAkce
Velké Karlovice
n3:mistoVydani
Los Alamitos
n3:nazevZdroje
Proceedings of the 2014 15th International Carpathian Control Conference, ICCC 2014
n3:obor
n16:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
8
n3:rokUplatneniVysledku
n17:2014
n3:tvurceVysledku
Poulain, M. Minárik, S. Legouera, M. Jančíková, Zora Koštial, Pavol Bošák, O. Zimný, Ondřej Soltani, M. T.
n3:typAkce
n6:WRD
n3:zahajeniAkce
2014-05-28+02:00
s:numberOfPages
5
n20:hasPublisher
IEEE Computer Society
n9:isbn
978-1-4799-3528-4
n15:organizacniJednotka
27360