Accurate Modeling of Unusual Electronic Circuit Elements with Artificial Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00184468" target="_blank" >RIV/68407700:21230/11:00184468 - 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

Accurate Modeling of Unusual Electronic Circuit Elements with Artificial Neural Networks

Popis výsledku v původním jazyce

Nowadays, there are many electronic structures for which their models are necessary. However, in the recent PSpice programs, only a class of five types of the MESFET model is available. In the paper, a new method is suggested for modeling electronic structures by exclusive neural networks, or by corrective neural networks working attached to a modified analytic model. The accuracy the analytic model is assessed by extracting the model parameters of MESFET, pHEMT, and microwave varactors. The accuracy ofprocedures that use the neural networks is assessed by extracting model parameters in static and dynamic domains. First, an approximation of the pHEMT output characteristics is carried out by means of both exclusive and corrective networks; and second,an approximation of the C-V function of an avalanche photodiode is performed by the exclusive network. Last, the memristor characteristic with an extraordinary hysteresis is approximated by a set of cooperative artificial neural networks.

Název v anglickém jazyce

Accurate Modeling of Unusual Electronic Circuit Elements with Artificial Neural Networks

Popis výsledku anglicky

Nowadays, there are many electronic structures for which their models are necessary. However, in the recent PSpice programs, only a class of five types of the MESFET model is available. In the paper, a new method is suggested for modeling electronic structures by exclusive neural networks, or by corrective neural networks working attached to a modified analytic model. The accuracy the analytic model is assessed by extracting the model parameters of MESFET, pHEMT, and microwave varactors. The accuracy ofprocedures that use the neural networks is assessed by extracting model parameters in static and dynamic domains. First, an approximation of the pHEMT output characteristics is carried out by means of both exclusive and corrective networks; and second,an approximation of the C-V function of an avalanche photodiode is performed by the exclusive network. Last, the memristor characteristic with an extraordinary hysteresis is approximated by a set of cooperative artificial neural networks.

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

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)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of 2nd Conference on Circuits, Systems, Control, Signals

ISBN

978-1-61804-035-0

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

150-155

Název nakladatele

World Scientific and Engineering Academy and Society

Místo vydání

Athens

Místo konání akce

Praha

Datum konání akce

26. 9. 2011

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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