Publication detail

Broadband Design of Planar Transmission Lines: Feed-Forward Neural Approach Versus Recurrent One

RAIDA, Z.

Original Title

Broadband Design of Planar Transmission Lines: Feed-Forward Neural Approach Versus Recurrent One

English Title

Broadband Design of Planar Transmission Lines: Feed-Forward Neural Approach Versus Recurrent One

Type

conference paper

Language

en

Original Abstract

In the paper, exploitation of recurrent neural networks for broadband modeling of EM structures is discussed. As a representative of EM structures, a shielded microstrip transmission line in layered media is elected. The structure is numerically modeled by finite-element method in the frequency range from 10 GHz to 80 GHz. The numerical model acts as a teacher for a neural network representing the behavior of the structure. For modeling purposes, both the feed-forward neural networks (a static mapping of an input pattern to an output target) and the recurrent neural networks (a mapping of an input pattern to the series of output targets) are exploited. Both the feed-forward neural models and the recurrent ones are in detail compared from the point of view of accuracy, approximation abilities, CPU-time demands and elaborateness of the development. The winner of the comparison is used for a broadband genetic optimization of the structure in order to demonstrate computational efficiency of the neural model.

English abstract

In the paper, exploitation of recurrent neural networks for broadband modeling of EM structures is discussed. As a representative of EM structures, a shielded microstrip transmission line in layered media is elected. The structure is numerically modeled by finite-element method in the frequency range from 10 GHz to 80 GHz. The numerical model acts as a teacher for a neural network representing the behavior of the structure. For modeling purposes, both the feed-forward neural networks (a static mapping of an input pattern to an output target) and the recurrent neural networks (a mapping of an input pattern to the series of output targets) are exploited. Both the feed-forward neural models and the recurrent ones are in detail compared from the point of view of accuracy, approximation abilities, CPU-time demands and elaborateness of the development. The winner of the comparison is used for a broadband genetic optimization of the structure in order to demonstrate computational efficiency of the neural model.

Keywords

microstrip transmission lines, approximation, look-up tables, feed-forward neural networks, recurrent neural networks

RIV year

2003

Released

07.09.2003

Publisher

Polytecnico di Torino

Location

Torino

ISBN

8882020088

Book

Proceedings of the International Conference on Electromagnetics in Advanced Applications

Pages from

155

Pages to

158

Pages count

4

Documents

BibTex


@inproceedings{BUT8194,
  author="Zbyněk {Raida}",
  title="Broadband Design of Planar Transmission Lines: Feed-Forward Neural Approach Versus Recurrent One",
  annote="In the paper, exploitation of recurrent neural networks for broadband modeling of EM structures is discussed. As a representative of EM structures, a shielded microstrip transmission line in layered media is elected. The structure is numerically modeled by finite-element method in the frequency range from 10 GHz to 80 GHz. The numerical model acts as a teacher for a neural network representing the behavior of the structure. For modeling purposes, both the feed-forward neural networks (a static mapping of an input pattern to an output target) and the recurrent neural networks (a mapping of an input pattern to the series of output targets) are exploited. Both the feed-forward neural models and the recurrent ones are in detail compared from the point of view of accuracy, approximation abilities, CPU-time demands and elaborateness of the development. The winner of the comparison is used for a broadband genetic optimization of the structure in order to demonstrate computational efficiency of the neural model.",
  address="Polytecnico di Torino",
  booktitle="Proceedings of the International Conference on Electromagnetics in Advanced Applications",
  chapter="8194",
  institution="Polytecnico di Torino",
  year="2003",
  month="september",
  pages="155",
  publisher="Polytecnico di Torino",
  type="conference paper"
}