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

conference paper

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

17. 12. 2004

Publisher

The World Scientific and Egineering Academy and Society

Location

Puerto De La Cruz, Tenerife

ISBN

960-8457-06-8

Book

Proceeding of the 4th WSEAS International Conference on Applied Informatics and Communications

Edition number

1

Pages from

156

Pages to

160

Pages count

5

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"
}