Mehmet Erler

Artificial Neural Networks for the Resonant Resistance Calculation of Electrically Thin and Thick Rectangular Microstrip Antennas

A new method for calculating the resonant resistance of electrically thin and thick rectangular microstrip patch antennas, based on the artificial neural networks, is presented. The four learning algorithms, the backpropagation, the delta-bar-delta, the quick propagation, and the extended-delta-bar-delta, are used to train the networks. The theoretical resonant resistance results obtained by using this method are in very good agreement with the experimental results available in the literature.A new method for calculating the resonant resistance of electrically thin and thick rectangular microstrip patch antennas, based on the artificial neural networks, is presented. The four learning algorithms, the backpropagation, the delta-bar-delta, the quick propagation, and the extended-delta-bar-delta, are used to train the networks. The theoretical resonant resistance results obtained by using this method are in very good agreement with the experimental results available in the literature.

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

Neural Computing of the Bandwith of Resonant Rectangular Microstrip Antennas

A new method based on the backpropagation multilayered perceptron network for calculating the bandwidth of resonant rectangular microstrip patch antennas is presented The method can be used for a wide range of substrate thicknesses and permittivities, and is useful for the computer-aided design (CAD) of microstrip antennas. The results obtained by using this new method are in conformity with those reported elsewhere. This method may find wide applications in high-frequency printed antennas, especially at the millimeter-wave frequency range.