MLP and KNN Algorithm Model Applications for Determining the Operating Frequency of A-Shaped Patch Antennas

In this study, two machine learning methods, namely multilayer perceptron (MLP) and K-nearest neighbors (KNN) algorithm models are used for determining the operating frequency of A-shaped patch antennas (APAs) at UHF band applications. Firstly, data set is obtained from the 144 antenna simulations using IE3D™ software based on method of moment (MoM). Weka (Waikato Environment for Knowledge Analysis) program was then used to build models by considering 144 simulation data. The models input with the various dimensions and electrical parameters of 124 APAs are trained and their accuracies are tested via 20 APAs. The mean absolute error (MAE) values are calculated for different number of hidden layer neurons and different neighbourhood values in MLP and KNN models, respectively.  The performance of the MLP and KNN models are compared in the training and testing process. The lowest MAEs are obtained with 6 hidden layer neurons for MLP and 2 neighbourhood values for KNN. These results point out that this models can be successfully applied to computation operating frequencies of APAs.