Result Details
Genetic Neural Networks for Modeling Dipole Antennas
ŠMÍD, P., RAIDA, Z., LUKEŠ, Z. Genetic Neural Networks for Modeling Dipole Antennas. In Proceeding of the 4th WSEAS International Conference on Applied Informatics and Communications. Puerto De La Cruz, Tenerife: The World Scientific and Egineering Academy and Society, 2004. 5 p. ISBN: 960-8457-06-8.
Type
conference paper
Language
English
Authors
Abstract
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 feed-forward one, are implemented in MATLAB. Results of training abilities are discussed.
Keywords
artificial neural networks, genetic algorithm, wire dipole antenna
Published
2004
Pages
5
Proceedings
Proceeding of the 4th WSEAS International Conference on Applied Informatics and Communications
Edition
1
Conference
4th WSEAS International Conference on
Applied Informatics and Communications
ISBN
960-8457-06-8
Publisher
The World Scientific and Egineering Academy and Society
Place
Puerto De La Cruz, Tenerife
BibTeX
@inproceedings{BUT12162,
author="Petr {Šmíd} and Zbyněk {Raida} and Zbyněk {Lukeš}",
title="Genetic Neural Networks for Modeling Dipole Antennas",
booktitle="Proceeding of the 4th WSEAS International Conference on Applied Informatics and Communications",
year="2004",
number="1",
pages="5",
publisher="The World Scientific and Egineering Academy and Society",
address="Puerto De La Cruz, Tenerife",
isbn="960-8457-06-8"
}
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