Selecting an Optimal Structure of Artificial Neural Networks for Characterizing RF Semiconductor Devices

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F10%3A00171283" target="_blank" >RIV/68407700:21230/10:00171283 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Selecting an Optimal Structure of Artificial Neural Networks for Characterizing RF Semiconductor Devices

Popis výsledku v původním jazyce

In the paper, a method is suggested for modeling miscellaneous RF semiconductor devices by exclusive neural networks or by corrective neural networks working attached to a modified analytic model. An accuracy of the proposed modification of the analyticmodel is assessed by extracting model parameters of the AlGaAs/InGaAs/GaAs pHEMT. An accuracy of procedures with neural networks is generally assessed by extracting their parameters in static and dynamic domains. An approximation of the AlGaAs/InGaAs/GaAs pHEMT output characteristics is carried out by means of both exclusive and corrective artificial neural networks. A systematic sequence of analyses is also performed for examining an optimal structure of the artificial neural network from the point ofview its structure and complexity. The tests have been performed on both five- and four-layer artificial neural networks that serve for modeling a P-channel JFET and for the AlGaAs/InGaAs/GaAs pHEMT.

Název v anglickém jazyce

Selecting an Optimal Structure of Artificial Neural Networks for Characterizing RF Semiconductor Devices

Popis výsledku anglicky

In the paper, a method is suggested for modeling miscellaneous RF semiconductor devices by exclusive neural networks or by corrective neural networks working attached to a modified analytic model. An accuracy of the proposed modification of the analyticmodel is assessed by extracting model parameters of the AlGaAs/InGaAs/GaAs pHEMT. An accuracy of procedures with neural networks is generally assessed by extracting their parameters in static and dynamic domains. An approximation of the AlGaAs/InGaAs/GaAs pHEMT output characteristics is carried out by means of both exclusive and corrective artificial neural networks. A systematic sequence of analyses is also performed for examining an optimal structure of the artificial neural network from the point ofview its structure and complexity. The tests have been performed on both five- and four-layer artificial neural networks that serve for modeling a P-channel JFET and for the AlGaAs/InGaAs/GaAs pHEMT.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GAP102%2F10%2F1614" target="_blank" >GAP102/10/1614: Memristivní, memkapacitivní a meminduktivní systémy: základní výzkum, modelování a simulace</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 53rd IEEE International Midwest Symposium on Circuits and Systems

ISBN

978-1-4244-7773-9

ISSN

1548-3746

e-ISSN

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Počet stran výsledku

4

Strana od-do

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Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Seattle, Washington

Datum konání akce

1. 8. 2010

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000287099800290