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ŠMÍD, P., RAIDA, Z., LUKEŠ, Z.
Originální název
Genetic Neural Networks for Modeling Dipole Antennas
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
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.
Klíčová slova
artificial neural networks, genetic algorithm, wire dipole antenna
Autoři
Rok RIV
2004
Vydáno
17. 12. 2004
Nakladatel
The World Scientific and Egineering Academy and Society
Místo
Puerto De La Cruz, Tenerife
ISBN
960-8457-06-8
Kniha
Proceeding of the 4th WSEAS International Conference on Applied Informatics and Communications
Číslo edice
1
Strany od
156
Strany do
160
Strany počet
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" }