Publication detail

Genetic Neural Networks for Modeling Dipole Antennas

ŠMÍD, P., RAIDA, Z., LUKEŠ, Z.

Original Title

Genetic Neural Networks for Modeling Dipole Antennas

English Title

Genetic Neural Networks for Modeling Dipole Antennas

Type

journal article - other

Language

en

Original 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.

English 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

RIV year

2004

Released

01.12.2004

Location

Puerto De La Cruz, Tenerife

Pages from

1868

Pages to

1872

Pages count

5

BibTex


@article{BUT45635,
  author="Petr {Šmíd} and Zbyněk {Raida} and Zbyněk {Lukeš}",
  title="Genetic Neural Networks for Modeling Dipole Antennas",
  annote="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.",
  booktitle="WSEAS Transactions on Computers, Issue 6, Volume 3, December 2004",
  chapter="45635",
  journal="WSEAS Transactions on Computers",
  number="3",
  volume="6",
  year="2004",
  month="december",
  pages="1868",
  type="journal article - other"
}