Genetic Neural Networks for Modeling Dipole Antennas

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F04%3APU46111" target="_blank" >RIV/00216305:26220/04:PU46111 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Genetic Neural Networks for Modeling Dipole Antennas

Original language description

The paper deals with original genetic neural networks for modeling wire dipole antennas. A novel approach to learning artificial neural networks (ANN) by genetic algorithms (GA) is described. The goal is to compare the learning abilities of neural antenna models trained by the GA and models trained by gradient algorithms. Developing the original design method based on genetic models of designed electromagnetic structures is the motivation of this work. Two types of ANN, the recurrent Elman ANN and the ffeed-forward one, are implemented in MATLAB. Results of training abilities are discussed.

Czech name

Genetické neuronové sítě pro modelování drátového dipólu

Czech description

V článku jsou pospány genetické neuronové sítě, které modelují drátový dipól. Článek je zaměřen zejména na trénování těchto sítí. Trénovací schopnosti jsou porovnány s gradientními metodami učení neuronových sítí.

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JA - Electronics and optoelectronics

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2004

Confidentiality

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

Name of the periodical

WSEAS Transactions on Computers

ISSN

1109-2750

e-ISSN

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Volume of the periodical

6

Issue of the periodical within the volume

3

Country of publishing house

GR - GREECE

Number of pages

5

Pages from-to

1868-1872

UT code for WoS article

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EID of the result in the Scopus database

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