Result Details

Genetic Neural Networks for Modeling Dipole Antennas

ŠMÍD, P., RAIDA, Z., LUKEŠ, Z. Genetic Neural Networks for Modeling Dipole Antennas. WSEAS Transactions on Computers, 2004, vol. 6, no. 3, 5 p. ISSN: 1109-2750.
Type
journal article
Language
English
Authors
Šmíd Petr, Ing., Ph.D.
Raida Zbyněk, prof. Dr. Ing., UREL (FEEC)
Lukeš Zbyněk, Ing., Ph.D.
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
Journal
WSEAS Transactions on Computers, vol. 6, no. 3, ISSN 1109-2750
Book
WSEAS Transactions on Computers, Issue 6, Volume 3, December 2004
Conference
4th WSEAS International Conference on Applied Informatics and Communications
Place
Puerto De La Cruz, Tenerife
BibTeX 
@article{BUT45635,
  author="Petr {Šmíd} and Zbyněk {Raida} and Zbyněk {Lukeš}",
  title="Genetic Neural Networks for Modeling Dipole Antennas",
  journal="WSEAS Transactions on Computers",
  year="2004",
  volume="6",
  number="3",
  pages="5",
  issn="1109-2750"
}
Departments
Department of Radio Electronics (UREL)
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