Publication detail

Genetic Neural Networks for Modeling Dipole Antennas

ŠMÍD, P., RAIDA, Z., LUKEŠ, Z.

Original Title

Genetic Neural Networks for Modeling Dipole Antennas

Type

journal article - other

Language

English

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.

Keywords

artificial neural networks, genetic algorithm, wire dipole antenna

Authors

ŠMÍD, P., RAIDA, Z., LUKEŠ, Z.

RIV year

2004

Released

1. 12. 2004

Location

Puerto De La Cruz, Tenerife

ISBN

1109-2750

Periodical

WSEAS Transactions on Computers

Year of study

6

Number

3

State

Hellenic Republic

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",
  journal="WSEAS Transactions on Computers",
  year="2004",
  volume="6",
  number="3",
  pages="5",
  issn="1109-2750"
}