Feed forward neural network characterization of circular SIW resonators

Author

Angiulli, G. ; Arnieri, E. ; De Carlo, D. ; Amendola, G.

Author_Institution

DIMET, Univ. Mediterranea, Reggio di Calabria

fYear

2008

fDate

5-11 July 2008

Firstpage

1

Lastpage

4

Abstract

In recent years Artificial Neural Networks have been adopted as an alternative modelling approach for the design of microwave circuits. In this work a characterization of circular resonators realized in substrate integrated waveguide (SIW) technology by means of Artificial Neural Networks is presented. SIW resonators are analyzed considering the scattering of the ensemble of metallic vias placed in a parallel plate waveguide. Resonances are efficiently located looking at an estimate of the smallest singular value. Results carried out for circular resonators demonstrate the effectiveness of the method.

Keywords

electrical engineering computing; feedforward neural nets; parallel plate waveguides; resonators; artificial neural networks; circular SIW resonators; feed forward neural network; microwave circuits; parallel plate waveguide; substrate integrated waveguide technology; Artificial neural networks; Feedforward neural networks; Feeds; Matrix decomposition; Neural networks; Resonance; Resonant frequency; Scattering; Tellurium; Transmission line matrix methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Antennas and Propagation Society International Symposium, 2008. AP-S 2008. IEEE

Conference_Location

San Diego, CA

Print_ISBN

978-1-4244-2041-4

Electronic_ISBN

978-1-4244-2042-1

Type

conf

DOI

10.1109/APS.2008.4619807

Filename

4619807