A comparative study of neural networks for input resistance computation of electrically thin and thick rectangular microstrip antennas

Abstract

Multilayered-perceptron-based neural models for calculating the input resistance of electrically thin and thick rectangular microstrip antennas are presented. Eleven learning algorithms—Levenberg-Marquardt, conjugate gradient of Fletcher-Reeves, Bayesian regularization, Broyden-Fletcher-Goldfarb-Shanno, conjugate gradient of Polak-Ribiere, conjugate gradient of Powell-Beale, scaled conjugate gradient, one-step secant, resilient backpropagation, backpropagation with momentum, and backpropagation with adaptive learning rate—are used to train the multilayered perceptrons. The input resistance results obtained with the use of 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 the Levenberg-Marquardt algorithm.