Unraveling Frequency Dynamics: Machine Learning Approach for predicting H-Shaped Microstrip Antenna’s resonant frequency

This paper addresses the regression problem of predicting the resonant frequency of H -shaped microstrip antennas using theoretical data generated through an enhanced cavity model method plus, a set of measured and simulated data from literature. Three machine learning algorithms; Multiple Layer Perceptron (MLP), LightGBM, and XGBoost are employed. The study begins with exploring the algorithms, including data generation and preprocessing, followed by a comparison with existing literature. An analysis of loss, score, and CPU execution time for each algorithm is provided. Finally, the paper demonstrates a conclusion for the application of these techniques.