Optimization of Rectangular Patch Antenna Using Machine Learning Algorithms

  • B. Naresh Kumar, Sai Uttam Gadde, Bhagyesh Reddy

Abstract

In this work, machine learning algorithms such as Multiple Linear Regression, Support Vector Regression, Decision Tree, Random Forest, and Artificial Neural Network are applied for optimization of rectangular microstrip patch antenna which is designed using IE3D Electromagnetic Simulation tool. The data is generated by performing multiple simulations for rectangular patch geometry using IE3D Electromagnetic Simulation tool which is based on method of moments. Generated data set include width, length and thickness of the patch, dielectric constant and frequency which are applied to different machine learning algorithms. The machine learning algorithms are trained to predict the best operating frequency for the given parameters. Mean absolute error (MAE) and Mean squared error (MSE) is evaluated metrics, which are obtained using different machine learning algorithms. The machine learning algorithm with low MAE and MSE are considered for results comparison with IE3D simulation results. Predicted values obtained using machine learning algorithm are accurate and in close agreement with the simulation findings.

Published
2021-06-15
How to Cite
B. Naresh Kumar, Sai Uttam Gadde, Bhagyesh Reddy. (2021). Optimization of Rectangular Patch Antenna Using Machine Learning Algorithms. Design Engineering, 910 - 920. Retrieved from http://thedesignengineering.com/index.php/DE/article/view/2069
Section
Articles