Artificial neural network for computing the resonant frequency of circular patch antennas

✍ Scribed by A. Ouchar; R. Aksas; H. Baudrand

• Publisher: John Wiley and Sons
• Year: 2005
• Tongue: English
• Weight: 143 KB
• Volume: 47
• Category: Article
• ISSN: 0895-2477
• DOI: 10.1002/mop.21230

✦ Synopsis

Abstract

In this paper, an artificial neural network (ANN) model for computing the resonant frequency of circular patch antennas (CPA) based on the feed‐forward network and back‐propagation algorithm is presented. The network is trained with the data obtained from CPA measurements. The network is trained by varying the patch radius, the height of the substrate, and the dielectric permittivity. The parameters of the proposed ANN model are the learning rate (0.0125), the number of epochs for training (399), the performance goal (1 × 10^−5^) and the parameters for the network, the number of hidden layers (two), with 20 neurons for each hidden layer, three neurons for the input layer, and a single neuron in the output layer. the results obtained from the trained neural network are compared with the values obtained from mathematical computations and from measurements presented in other works. The proposed ANN with two hidden layers gives accurate results and requires no additional training epochs. © 2005 Wiley Periodicals, Inc. Microwave Opt Technol Lett 47: 564–566, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21230