Optimization of Circularly Polarized Omnidirectional Base Station Antenna by Machine Learning

Abstract

Abstract Machine learning (ML) is a scientific technology related to data learning that can help machines learn patterns from existing complex data to predict future behavioral outcomes and trends. In this paper, we use 16 different ML algorithms and 5 different hyper-parameters optimal methods to model a circularly polarized omnidirectional base station antenna. The mean absolute error is 8.431, the root mean squared error is 11.100, the coefficient of determination is 0.967, and the adjusted coefficient of determination is 0.944, which means evaluation metrics are excellent.