Publication detail

Modeling broadband microwave structures by artificial neural networks

RAIDA, Z., LUKEŠ, Z., OTEVŘEL, V.

Original Title

Modeling broadband microwave structures by artificial neural networks

English Title

Modeling broadband microwave structures by artificial neural networks

Type

journal article - other

Language

en

Original Abstract

The paper describes the exploitation of feed-forward neural networks and recurrent neural networks for replacing full-wave numerical models of microwave structures in complex microwave design tools. Building a neural model, attention is turned to the modeling accuracy and to the efficiency of building a model. Dealing with the accuracy, we describe a method of increasing it by successive completing a training set. Neural models are mutually compared in order to highlight their advantages and disadvantages. As a reference model for comparisons, approximations based on standard cubic splines are used. Neural models are used to replace both the time-domain numeric models and the frequency-domain ones.

English abstract

The paper describes the exploitation of feed-forward neural networks and recurrent neural networks for replacing full-wave numerical models of microwave structures in complex microwave design tools. Building a neural model, attention is turned to the modeling accuracy and to the efficiency of building a model. Dealing with the accuracy, we describe a method of increasing it by successive completing a training set. Neural models are mutually compared in order to highlight their advantages and disadvantages. As a reference model for comparisons, approximations based on standard cubic splines are used. Neural models are used to replace both the time-domain numeric models and the frequency-domain ones.

Keywords

Artificial neural networks, frequency-domain finite elements, time-domain method of moments, wire antennas, microwave transmission lines.

RIV year

2004

Released

01.06.2004

Pages from

3

Pages to

11

Pages count

9

BibTex


@article{BUT42090,
  author="Zbyněk {Raida} and Zbyněk {Lukeš} and Viktor {Otevřel}",
  title="Modeling broadband microwave structures by artificial neural networks",
  annote="The paper describes the exploitation of feed-forward neural networks and recurrent neural networks for replacing full-wave numerical models of microwave structures in complex microwave design tools. Building a neural model, attention is turned to the modeling accuracy and to the efficiency of building a model. Dealing with the accuracy, we describe a method of increasing it by successive completing a training set.
Neural models are mutually compared in order to highlight their advantages and disadvantages. As a reference model for comparisons, approximations based on standard cubic splines are used.
Neural models are used to replace both the time-domain numeric models and the frequency-domain ones.",
  chapter="42090",
  journal="Radioengineering",
  number="2",
  volume="13",
  year="2004",
  month="june",
  pages="3",
  type="journal article - other"
}