Pre-Design of Multi-Band Planar Antennas by Artificial Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F24%3A00558955" target="_blank" >RIV/60162694:G43__/24:00558955 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26220/23:PU148289

Výsledek na webu

<a href="https://www.mdpi.com/2079-9292/12/6/1345" target="_blank" >https://www.mdpi.com/2079-9292/12/6/1345</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/electronics12061345" target="_blank" >10.3390/electronics12061345</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Pre-Design of Multi-Band Planar Antennas by Artificial Neural Networks

Popis výsledku v původním jazyce

In this communication, artificial neural networks are used to estimate the initial structure of a multiband planar antenna. The neural networks are trained on a set of selected normalized multiband antennas characterized by time-efficient modal analysis with limited accuracy. Using the Deep Learning Toolbox in Matlab, several types of neural networks have been created and trained on the sample planar multiband antennas. In the neural network learning process, suitable network types were selected for the design of these antennas. The trained networks, depending on the desired operating bands, will select the appropriate antenna geometry. This is further optimized using Newton's method in HFSS. The use of the neural pre-design concept speeds up and simplifies the design of multiband planar antennas. The findings presented in this paper will be used to refine and accelerate the design of planar multiband antennas.

Název v anglickém jazyce

Pre-Design of Multi-Band Planar Antennas by Artificial Neural Networks

Popis výsledku anglicky

In this communication, artificial neural networks are used to estimate the initial structure of a multiband planar antenna. The neural networks are trained on a set of selected normalized multiband antennas characterized by time-efficient modal analysis with limited accuracy. Using the Deep Learning Toolbox in Matlab, several types of neural networks have been created and trained on the sample planar multiband antennas. In the neural network learning process, suitable network types were selected for the design of these antennas. The trained networks, depending on the desired operating bands, will select the appropriate antenna geometry. This is further optimized using Newton's method in HFSS. The use of the neural pre-design concept speeds up and simplifies the design of multiband planar antennas. The findings presented in this paper will be used to refine and accelerate the design of planar multiband antennas.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

ELECTRONICS

ISSN

2079-9292

e-ISSN

2079-9292

Svazek periodika

12

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

11

Strana od-do

1345

Kód UT WoS článku

000956815500001

EID výsledku v databázi Scopus

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