NEURAL NETWORKS FOR THE CALCULATION OF BANDWIDTH OF RECTANGULAR MICROSTRIP ANTENNAS
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NEURAL NETWORKS FOR THE CALCULATION OF BANDWIDTH OF RECTANGULAR MICROSTRIP ANTENNASAbstract
Neural models for calculating the bandwidth of electrically thin and thick rectangular microstrip antennas, based on the multilayered perceptrons and the radial basis function networks, are presented. Thirteen learning algorithms, the conjugate gradient of Fletcher-Reeves, Levenberg-Marquardt, scaled conjugate gradient, resilient backpropagation, conjugate gradient of Powell-Beale, conjugate gradient of Polak-Ribiére, bayesian regularization, one-step secant, backpropagation with adaptive learning rate, Broyden-Fletcher-Goldfarb-Shanno, backpropagation with momentum, directed random search and genetic algorithm, are used to train the multilayered perceptrons. The radial basis function network is trained by the extended delta-bar-delta algorithm. The bandwidth 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 results for training and test were obtained from the multilayered perceptrons trained by the conjugate gradient of Powell-Beale and Broyden-Fletcher-Goldfarb-Shanno algorithms, respectively.
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