Comparison of neural networks for resonant frequency computation of electrically thin and thick rectangular microstrip antennas

Neural models for calculating the resonant frequency of electrically thin and thick rectangular microstrip antennas, based on the multilayered perceptrons and the radial basis function networks, are presented. Six learning algorithms, backpropagation, 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 reason for using six different learning algorithms and two different structures is to speed up the training time and to compare the performance of neural models for this specific application. 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 results for training and test were obtained from the radial basis function network and the multilayered perceptrons trained by extended-delta-bar-delta algorithm, respectively

Calculation of bandwidth for electrically thin and thick rectangular microstrip antennas with the use of multilayered perceptrons

A new, simple approach for calculating bandwidth of electrically thin and thick rectangular microstrip patch antennas, based on multilayered perceptrons network, is presented. Four learning algorithms, backpropagation, delta-bar-delta, quick propagation, and extended-delta-bar-delta are used to train perceptrons. Theoretical bandwidth results obtained using the new approach are in agreement with experimental results available in the literature. (C) 1999 John Wiley & Sons, Inc