ARTIFICIAL NEURAL NETWORKS FOR RESONANT FREQUENCY CALCULATION OF RECTANGULAR MICROSTRIP ANTENNAS WITH THIN AND THICK SUBSTRATES

Authors: K. Guney¹; S. S. Gultekin²

Source: International Journal of Infrared and Millimeter Waves, Volume 25, Number 9, September 2004 , pp. 1383-1399(17)

Publisher: Springer

Buy & download fulltext article:

Price: $47.00 plus tax (Refund Policy)

Abstract:

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-Reeves, conjugate gradient of Powell-Beale, bayesian regularization, scaled conjugate gradient, Broyden-Fletcher-Goldfarb-Shanno, resilient backpropagation, conjugate of Polak-Ribiére, 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.

Keywords: Neural networks; microstrip antenna; resonant frequency

Document Type: Research article

DOI: http://dx.doi.org/10.1023/B:IJIM.0000045146.70836.ee

Affiliations: 1: Electronic Engineering Department, Faculty of Engineering, Erciyes University, Kayseri, 38039, Turkey, Email: kguney@erciyes.edu.tr 2: Electric and Electonic Engineering Department, Faculty of Engineering and Architecture, Selcuk University, 42031, Konya, Turkey, Email: sgultekin@selcuk.edu.tr

Publication date: 2004-09-01