Neural Network-Based CAD Model for the Design of Hemispherical Dielectric Resonator Antenna with a Concentric Conductor

Author

Zainud-Deen, S.H. ; Ibrahim, I.I. ; Ibrahem, Sabiry M M ; Hassan, A.S.

Author_Institution

Fac. of Electron. Eng., Menoufia Univ.

Volume

0

fYear

2006

fDate

14-16 March 2006

Firstpage

1

Lastpage

7

Abstract

Radial-basis function neural network (RBF-NN) is presented for calculating the dimensions of the probe-fed hemispherical dielectric-resonator antenna (DRA) with a concentric conductor. Simple formulas for the resonant frequency and the Q-factor are used. The RBF-NN is trained to calculate the resonant frequency and the Q-factor of the DRA antenna, when the inputs are the conductor radius and the relative dielectric constant. The RBF-NN is also trained to calculate the conductor radius when the resonant frequency and the Q-factor are known

Keywords

CAD; Q-factor; antenna feeds; conducting bodies; dielectric resonator antennas; electrical engineering computing; permittivity; radial basis function networks; CAD model; Q-factor; RBF-NN; concentric conductor; dielectric constant; dielectric-resonator antenna; probe-fed hemispherical DRA; radial-basis function neural network; resonant frequency; Antenna feeds; Artificial neural networks; Biological neural networks; Conductors; Design automation; Dielectric constant; Dielectric resonator antennas; Neural networks; Q factor; Resonant frequency;

fLanguage

English

Publisher

ieee

Conference_Titel

Radio Science Conference, 2006. NRSC 2006. Proceedings of the Twenty Third National

Conference_Location

Menoufiya

Print_ISBN

977-5031-84-2

Electronic_ISBN

977-5031-84-2

Type

conf

DOI

10.1109/NRSC.2006.386323

Filename

4275120