Publication detail

Planar Transmission Lines of Atypical Cross-Section: Shape Optimization Exploring Low Accuracy Neural Networks

RAIDA, Z.

Original Title

Planar Transmission Lines of Atypical Cross-Section: Shape Optimization Exploring Low Accuracy Neural Networks

English Title

Planar Transmission Lines of Atypical Cross-Section: Shape Optimization Exploring Low Accuracy Neural Networks

Type

conference paper

Language

en

Original Abstract

In the paper, optimization of the shape and of the size of the shielded microstrip line is described. Reaching prescribed dispersion characteristics is the goal of the optimization. CPU-time demands of the optimization procedure are minimized using efficiently trained neural networks. Genetic algorithms estimate regions of the optimization space, which might contain a global minimum. Detailed investigation of the suspicious regions is performed by Newton’s method.

English abstract

In the paper, optimization of the shape and of the size of the shielded microstrip line is described. Reaching prescribed dispersion characteristics is the goal of the optimization. CPU-time demands of the optimization procedure are minimized using efficiently trained neural networks. Genetic algorithms estimate regions of the optimization space, which might contain a global minimum. Detailed investigation of the suspicious regions is performed by Newton’s method.

RIV year

2001

Released

10.09.2001

Publisher

Polytecnico di Torino

Location

Torino (Italy)

ISBN

8882020983

Book

Proceedings of the International Conference on Electromagnetics in Advanced Applications ICEAA 2001

Pages from

627

Pages to

630

Pages count

4

BibTex


@inproceedings{BUT3049,
  author="Zbyněk {Raida}",
  title="Planar Transmission Lines of Atypical Cross-Section: Shape Optimization Exploring Low Accuracy Neural Networks",
  annote="In the paper, optimization of the shape and of the size of the shielded microstrip line is described. Reaching prescribed dispersion characteristics is the goal of the optimization. CPU-time demands of the optimization procedure are minimized using efficiently trained neural networks. Genetic algorithms estimate regions of the optimization space, which might contain a global minimum. Detailed investigation of the suspicious regions is performed by Newton’s method.",
  address="Polytecnico di Torino",
  booktitle="Proceedings of the International Conference on Electromagnetics in Advanced Applications ICEAA 2001",
  chapter="3049",
  institution="Polytecnico di Torino",
  year="2001",
  month="september",
  pages="627",
  publisher="Polytecnico di Torino",
  type="conference paper"
}