Broadband Design of Planar Transmission Lines: Feed-Forward Neural Approach Versus Recurrent One

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Broadband Design of Planar Transmission Lines: Feed-Forward Neural Approach Versus Recurrent One

Popis výsledku v původním jazyce

In the paper, exploitation of recurrent neural networks for broadband modeling of EM structures is discussed. As a representative of EM structures, a shielded microstrip transmission line in layered media is elected. The structure is numerically modeledby finite-element method in the frequency range from 10 GHz to 80 GHz. The numerical model acts as a teacher for a neural network representing the behavior of the structure. For modeling purposes, both the feed-forward neural networks (a static mapping oof an input pattern to an output target) and the recurrent neural networks (a mapping of an input pattern to the series of output targets) are exploited. Both the feed-forward neural models and the recurrent ones are in detail compared from the point ofview of accuracy, approximation abilities, CPU-time demands and elaborateness of the development. The winner of the comparison is used for a broadband genetic optimization of the structure in order to demonstrate computational efficiency

Název v anglickém jazyce

Broadband Design of Planar Transmission Lines: Feed-Forward Neural Approach Versus Recurrent One

Popis výsledku anglicky

In the paper, exploitation of recurrent neural networks for broadband modeling of EM structures is discussed. As a representative of EM structures, a shielded microstrip transmission line in layered media is elected. The structure is numerically modeledby finite-element method in the frequency range from 10 GHz to 80 GHz. The numerical model acts as a teacher for a neural network representing the behavior of the structure. For modeling purposes, both the feed-forward neural networks (a static mapping oof an input pattern to an output target) and the recurrent neural networks (a mapping of an input pattern to the series of output targets) are exploited. Both the feed-forward neural models and the recurrent ones are in detail compared from the point ofview of accuracy, approximation abilities, CPU-time demands and elaborateness of the development. The winner of the comparison is used for a broadband genetic optimization of the structure in order to demonstrate computational efficiency

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2003

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the International Conference on Electromagnetics in Advanced Applications

ISBN

8882020088

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

155-158

Název nakladatele

Polytecnico di Torino

Místo vydání

Torino

Místo konání akce

Torino

Datum konání akce

8. 9. 2003

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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