Detail publikace

Genetic Neural Networks for Modeling Dipole Antennas

Originální název

Genetic Neural Networks for Modeling Dipole Antennas

Anglický název

Genetic Neural Networks for Modeling Dipole Antennas

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

BibTex


@inproceedings{BUT12162,
  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.",
  address="The World Scientific and Egineering Academy and Society",
  booktitle="Proceeding of the 4th WSEAS International Conference on Applied Informatics and Communications",
  chapter="12162",
  institution="The World Scientific and Egineering Academy and Society",
  year="2004",
  month="december",
  pages="156",
  publisher="The World Scientific and Egineering Academy and Society",
  type="conference paper"
}