Publication detail

Neural Model of Dipole Antenna – Genetic Algorithm for Training Artificial Neural Networks with Backpropagation

Petr Šmíd, Zbyněk Raida

Original Title

Neural Model of Dipole Antenna – Genetic Algorithm for Training Artificial Neural Networks with Backpropagation

English Title

Neural Model of Dipole Antenna – Genetic Algorithm for Training Artificial Neural Networks with Backpropagation

Type

conference paper

Language

en

Original Abstract

The paper deals with training the neural models of microwave structures. The first, an artificial neural network (ANN) is trained with basic genetic algorithm (GA). Training abilities are discussed. Further, the modification of GA and an approach to learning artificial neural networks (ANN) with backpropagation is described. Neural networks are implemented in MATLAB. Results of training abilities are compared. Finally, some ideas for improving the training process are mentioned.

English abstract

The paper deals with training the neural models of microwave structures. The first, an artificial neural network (ANN) is trained with basic genetic algorithm (GA). Training abilities are discussed. Further, the modification of GA and an approach to learning artificial neural networks (ANN) with backpropagation is described. Neural networks are implemented in MATLAB. Results of training abilities are compared. Finally, some ideas for improving the training process are mentioned.

Keywords

Artificial Neural Network, Genetic Algorithm

RIV year

2005

Released

01.01.2005

Publisher

BUT, FEEC, Institute or Radio Electronics

ISBN

80-214-2904-6

Book

15-th International Czech - Slovak Scientific Conference Radioelektronika 2005

Pages from

227

Pages to

230

Pages count

4

BibTex


@inproceedings{BUT14759,
  author="Petr {Šmíd} and Zbyněk {Raida}",
  title="Neural Model of Dipole Antenna – Genetic Algorithm for Training Artificial Neural Networks with Backpropagation",
  annote="The paper deals with training the neural models of microwave structures. The first, an artificial neural network (ANN) is trained with basic genetic algorithm (GA). Training abilities are discussed. Further, the modification of GA and an approach to learning artificial neural networks (ANN) with backpropagation is described. Neural networks are implemented in MATLAB. Results of training abilities are compared. Finally, some ideas for improving the training process are mentioned.",
  address="BUT, FEEC, Institute or Radio Electronics",
  booktitle="15-th International Czech - Slovak Scientific Conference Radioelektronika 2005",
  chapter="14759",
  institution="BUT, FEEC, Institute or Radio Electronics",
  year="2005",
  month="january",
  pages="227",
  publisher="BUT, FEEC, Institute or Radio Electronics",
  type="conference paper"
}