Title :
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
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;
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
DOI :
10.1109/APS.2008.4619807
