Title :
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.
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;
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
DOI :
10.1109/NRSC.2006.386323
