Publication detail

Application of neural networks: enhancing efficiency of microwave design

Petr Šmíd, Zbyněk Raida

Original Title

Application of neural networks: enhancing efficiency of microwave design

English Title

Application of neural networks: enhancing efficiency of microwave design

Type

journal article - other

Language

en

Original Abstract

The paper describes the methodology of the automated creation of neural models of microwave structures. During the creation process, artificial neural networks are trained using the combination of the particle swarm optimization and the quasi-Newton method to avoid critical training problems of the conventional neural nets. Neural models are required being wideband. In order to meet this requirement, feed-forward neural networks and recurrent ones are used for modelling, and their properties are in detail mutually compared. In the paper, neural networks are used to approximate behaviour of a planar microwave filter (moment method, Zeland IE3D). In order to evaluate the efficiency of neural modelling, global optimizations are performed using numerical models and neural ones. Both approaches are compared from the viewpoint of CPU-time demands and accuracy. Considering conclusions, methodological recommendations for including neural networks to microwave design are formulated.

English abstract

The paper describes the methodology of the automated creation of neural models of microwave structures. During the creation process, artificial neural networks are trained using the combination of the particle swarm optimization and the quasi-Newton method to avoid critical training problems of the conventional neural nets. Neural models are required being wideband. In order to meet this requirement, feed-forward neural networks and recurrent ones are used for modelling, and their properties are in detail mutually compared. In the paper, neural networks are used to approximate behaviour of a planar microwave filter (moment method, Zeland IE3D). In order to evaluate the efficiency of neural modelling, global optimizations are performed using numerical models and neural ones. Both approaches are compared from the viewpoint of CPU-time demands and accuracy. Considering conclusions, methodological recommendations for including neural networks to microwave design are formulated.

Keywords

Feed-forward neural networks, recurrent neural networks, quasi-Newton methods, particle swarm optimization

RIV year

2006

Released

01.06.2006

Pages from

2

Pages to

10

Pages count

9

BibTex


@article{BUT43189,
  author="Petr {Šmíd} and Zbyněk {Raida}",
  title="Application of neural networks: enhancing efficiency of microwave design",
  annote="The paper describes the methodology of the automated creation of neural models of microwave structures. During the creation process, artificial neural networks are trained using the combination of the particle swarm optimization and the quasi-Newton method to avoid critical training problems of the conventional neural nets.
Neural models are required being wideband. In order to meet this requirement, feed-forward neural networks and recurrent ones are used for modelling, and their properties are in detail mutually compared.
In the paper, neural networks are used to approximate behaviour of a planar microwave filter (moment method, Zeland IE3D). In order to evaluate the efficiency of neural modelling, global optimizations are performed using numerical models and neural ones. Both approaches are compared from the viewpoint of CPU-time demands and accuracy. Considering conclusions, methodological recommendations for including neural networks to microwave design are formulated.",
  chapter="43189",
  journal="Microwave Review (ISSN 1453-5835)",
  number="1",
  volume="12",
  year="2006",
  month="june",
  pages="2",
  type="journal article - other"
}