Precise Characterization of Memristive Systems by Cooperative Artificial Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00198153" target="_blank" >RIV/68407700:21230/12:00198153 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/SCIS-ISIS.2012.6505343" target="_blank" >http://dx.doi.org/10.1109/SCIS-ISIS.2012.6505343</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SCIS-ISIS.2012.6505343" target="_blank" >10.1109/SCIS-ISIS.2012.6505343</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Precise Characterization of Memristive Systems by Cooperative Artificial Neural Networks

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Precise Characterization of Memristive Systems by Cooperative Artificial Neural Networks

Popis výsledku anglicky

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

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

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 statě ve sborníku

The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligent Systems

ISBN

978-1-4673-2742-8

ISSN

1880-3741

e-ISSN

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Počet stran výsledku

4

Strana od-do

2130-2133

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Kobe

Datum konání akce

20. 11. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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