Artificial neural networks for resonant frequency calculation of rectangular microstrip antennas with thin and thick substrates
Yükleniyor...
Dosyalar
Tarih
2004
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
KLUWER ACADEMIC/PLENUM PUBL
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Neural models based on multilayered perceptrons for computing the resonant frequency of rectangular microstrip antennas with thin and thick substrates are presented. Eleven learning algorithms, Levenberg-Marquardt. conjugate gradient of Fletcher-Recvos., conjugate gradient of Powell-Beale, bayesian regularization.. scaled conjugate gradient, Broyden-Fletcher-Goldfarb-Shanno, resilient backpropagation, conjugate gradient of Polak-Ribiere. backpropagation with adaptive learning rate. one-step secant, and backpropagation with. momentum, are used to train the multilayered perceptrons. The resonant frequency results obtained by using neural models are in very good agreement with the experimental results available in the literature. When the performances of neural models are compared with each other, the best result is obtained from the multilayered perceptrons trained by Levenberg-Marquardt. algorithm.
Açıklama
Anahtar Kelimeler
neural networks, microstrip antenna, resonant frequency
Kaynak
INTERNATIONAL JOURNAL OF INFRARED AND MILLIMETER WAVES
WoS Q Değeri
Q3
Scopus Q Değeri
N/A
Cilt
25
Sayı
9
