Abstract
This article explores machine learning techniques, specifically Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Gaussian Process Regression (GPR), to predict the S11 parameter of a slotted square patch antenna optimized for Wireless Local Area Network (WLAN) operation between 5.6 GHz and 5.85 GHz. The antenna, measuring 30x30x1.6 mm? and centered at 5.725 GHz, features a coaxial probe feed design with a circular slot within the square patch to enhance bandwidth. These ML methods demonstrate superior efficiency compared to traditional simulation tools, enabling robust exploration of design configurations and accurate prediction of the antenna's electrical and physical characteristics. Notably, Gaussian Process Regression (GPR) consistently reveal lower Mean Squared Error (MSE) and higher R-squared (R?) values than ANN and SVM, suggesting superior accuracy in modeling the antenna's performance metrics.
Published Version
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