Design of an Ann Model Trained by Various Learning Algorithms to Compute the Operating Frequency of E-Shaped Patch Antennas

Ahmet KAYABAŞI, Abdurrahim TOKTAŞ, Ali AKDAĞLI
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Abstract


An artificial neural network (ANN) trained by different learning algorithms implemented to computing the operating frequency of E-shaped patch antennas (EPAs) is designed in this study. The ANN model is built on a multilayered perceptron (MLP) based on feed forward back propagation (FFBP). A data pool is firstly constituted for training and testing the ANN model through 144 EPA simulations using the moment method-based HyperLynx® 3D EM software in terms of the operating frequency. The ANN model is then trained via 130 data, and the accuracy of the model is tested through 14 data of simulated EPAs. The ANN is trained by 8 different learning algorithms to achieve a robust model. A benchmark which compares the learning algorithms against each other according to percentage error is revealed. The validity of the ANN is corroborated by simulated and measured data reported in the literature. It shows that the ANN model trained by Levenberg–Marquardt learning algorithm computes the closest results. The proposed ANN model can be successfully exploited to analyze the EPAs in views of the operating frequency.


Keywords


Antennas; patch antennas; artificial neural networks (ANN); operating frequency; learning algorithms

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