A COMPARATIVE STUDY OF NEURAL NETWORKS FOR RESONANT FREQUENCY COMPUTATION OF ELECTRICALLY THIN AND THICK CIRCULAR MICROSTRIP ANTENNAS

Neural models for calculating the resonant frequency of electrically thin and thick circular microstrip antennas, based on the multilayered perceptrons and the radial basis function networks, are presented. Five learning algorithms, delta-bar-delta, extended delta-bar-delta, quick-propagation, directed random search and genetic algorithms, are used to train the multilayered perceptrons. The radial basis function network is trained according to its learning strategy. The resonant frequency results obtained by using neural models are in very good agreement with the experimental results available in the literature.

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