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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F12%3A00198153%21RIV14-MSM-21230___
rdf:type
skos:Concept n14:Vysledek
dcterms:description
Nowadays, there are many emerging electronic structures for which their nonlinear models for CAD are necessary, especially for the ones from the area of nanoelectronics. However, for such structures, sufficiently accurate analytic models are mostly unavailable. This is partially caused by the fact that the physical principles of the element operation are sometimes not fully clear (especially for quantum devices), and also by bizarre characteristics of some elements (typically with irregularities and a hysteresis in parts of characteristics). In such cases, models based on artificial neural networks are necessary and useful for these elements. Majority of the elements can be characterized with a single artificial neural network. However, for certain kinds of elements, a cooperation of more artificial networks is necessary. This case is described in the paper, where the Pt - TiO_(2-x) - Pt memristor characteristic with an extraordinary (but typical) hysteresis is approximated by a set of cooperative artificial neural networks, as a single network is unable to characterize this unconventional element. Moreover, a semiautomatic selection of an optimal structure of the networks (both numbers of hidden layers and the numbers of the elements in the layers) is suggested in the paper. Nowadays, there are many emerging electronic structures for which their nonlinear models for CAD are necessary, especially for the ones from the area of nanoelectronics. However, for such structures, sufficiently accurate analytic models are mostly unavailable. This is partially caused by the fact that the physical principles of the element operation are sometimes not fully clear (especially for quantum devices), and also by bizarre characteristics of some elements (typically with irregularities and a hysteresis in parts of characteristics). In such cases, models based on artificial neural networks are necessary and useful for these elements. Majority of the elements can be characterized with a single artificial neural network. However, for certain kinds of elements, a cooperation of more artificial networks is necessary. This case is described in the paper, where the Pt - TiO_(2-x) - Pt memristor characteristic with an extraordinary (but typical) hysteresis is approximated by a set of cooperative artificial neural networks, as a single network is unable to characterize this unconventional element. Moreover, a semiautomatic selection of an optimal structure of the networks (both numbers of hidden layers and the numbers of the elements in the layers) is suggested in the paper.
dcterms:title
Precise Characterization of Memristive Systems by Cooperative Artificial Neural Networks Precise Characterization of Memristive Systems by Cooperative Artificial Neural Networks
skos:prefLabel
Precise Characterization of Memristive Systems by Cooperative Artificial Neural Networks Precise Characterization of Memristive Systems by Cooperative Artificial Neural Networks
skos:notation
RIV/68407700:21230/12:00198153!RIV14-MSM-21230___
n14:predkladatel
n15:orjk%3A21230
n3:aktivita
n7:S n7:P
n3:aktivity
P(GAP102/10/1614), S
n3:dodaniDat
n8:2014
n3:domaciTvurceVysledku
n4:7511124 Yadav, Abhimanyu n4:4816463
n3:druhVysledku
n12:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n17:predkladatel
n3:idSjednocenehoVysledku
160940
n3:idVysledku
RIV/68407700:21230/12:00198153
n3:jazykVysledku
n9:eng
n3:klicovaSlova
Artificial neural networks; training epochs; multilayer perceptron; device characterization; memristive system
n3:klicoveSlovo
n5:Artificial%20neural%20networks n5:device%20characterization n5:training%20epochs n5:memristive%20system n5:multilayer%20perceptron
n3:kontrolniKodProRIV
[6AEA1AE5BC00]
n3:mistoKonaniAkce
Kobe
n3:mistoVydani
Piscataway
n3:nazevZdroje
The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligent Systems
n3:obor
n18:JA
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n16:GAP102%2F10%2F1614
n3:rokUplatneniVysledku
n8:2012
n3:tvurceVysledku
Yadav, Abhimanyu Pospíšil, Ladislav Dobeš, Josef
n3:typAkce
n21:WRD
n3:zahajeniAkce
2012-11-20+01:00
s:issn
1880-3741
s:numberOfPages
4
n23:doi
10.1109/SCIS-ISIS.2012.6505343
n6:hasPublisher
IEEE
n22:isbn
978-1-4673-2742-8
n20:organizacniJednotka
21230