. "1109-2750" . . . . . . . "P(GA102/04/1079), P(GD102/03/H086), V, Z(MSM 262200011), Z(MSM 262200022)" . "[0FC136EA9FA4]" . . . "3" . "Genetic Neural Networks for Modeling Dipole Antennas" . "Genetic Neural Networks for Modeling Dipole Antennas"@en . "WSEAS Transactions on Computers" . "artificial neural networks, genetic algorithm, wire dipole antenna"@en . "26220" . "Genetic Neural Networks for Modeling Dipole Antennas" . . "Luke\u0161, Zbyn\u011Bk" . . "6" . . "5"^^ . "Genetic Neural Networks for Modeling Dipole Antennas"@en . "3"^^ . "3"^^ . "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." . . . . . . "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."@en . . "Raida, Zbyn\u011Bk" . "RIV/00216305:26220/04:PU46111" . . "RIV/00216305:26220/04:PU46111!RIV11-MSM-26220___" . "\u0160m\u00EDd, Petr" . . . . "565334" . "GR - \u0158eck\u00E1 republika" .