Characterization of Electronic Circuit Elements by Exclusive and Corrective Artificial Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00193966" target="_blank" >RIV/68407700:21230/12:00193966 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.naun.org/multimedia/NAUN//mcs/index.html" target="_blank" >http://www.naun.org/multimedia/NAUN//mcs/index.html</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Characterization of Electronic Circuit Elements by Exclusive and Corrective Artificial Neural Networks

Popis výsledku v původním jazyce

At present, there are many novel electronic circuit elements for which their nonlinear models for CAD are necessary, especially for microwave ones. However, in the PSpice-family programs, only a class of several classic types of the MESFET model is available for the microwave area. In the paper, a novel reliable way is suggested for modeling various electronic structures by exclusive neural networks, or by corrective neural networks working attached to a modified analytic model. The accuracy of the proposed modification of the analytic model is assessed by extracting the model parameters of GaAs MESFET, AlGaAs/InGaAs/GaAs pHEMT, and GaAs microwave varactors. First, a precise approximation of the pHEMT output characteristics is carried out by means of both exclusive and corrective artificial neural networks; and second, an approximation of the capacitance (C-V) function of the SACM InGaAs/InP avalanche photodiode is performed by the exclusive neural network. Further, the Pt-TiO_(2-x)-Pt

Název v anglickém jazyce

Characterization of Electronic Circuit Elements by Exclusive and Corrective Artificial Neural Networks

Popis výsledku anglicky

At present, there are many novel electronic circuit elements for which their nonlinear models for CAD are necessary, especially for microwave ones. However, in the PSpice-family programs, only a class of several classic types of the MESFET model is available for the microwave area. In the paper, a novel reliable way is suggested for modeling various electronic structures by exclusive neural networks, or by corrective neural networks working attached to a modified analytic model. The accuracy of the proposed modification of the analytic model is assessed by extracting the model parameters of GaAs MESFET, AlGaAs/InGaAs/GaAs pHEMT, and GaAs microwave varactors. First, a precise approximation of the pHEMT output characteristics is carried out by means of both exclusive and corrective artificial neural networks; and second, an approximation of the capacitance (C-V) function of the SACM InGaAs/InP avalanche photodiode is performed by the exclusive neural network. Further, the Pt-TiO_(2-x)-Pt

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

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

2012

Kód důvěrnosti údajů

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

Název periodika

International Journal of Mathematics and Computers in Simulation

ISSN

1998-0159

e-ISSN

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Svazek periodika

6

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

136-143

Kód UT WoS článku

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EID výsledku v databázi Scopus

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