Hybrid machine learning and Gaussian process for antenna parameter estimation

This paper presents a hybrid method of combining the Random Forest (RF) algorithm in machine learning (ML) and the Gaussian process (GP) to design microstrip patch antennas at any frequency from 0.6 to 6.5 GHz. Distinct from many published works, the proposed model is trained and tested with a high quality large dataset obtained from full-wave simulations in CST software, with experimental verification also performed. These factors are vital to assess the ML model's efficacy, but they haven't been thoroughly examined in other studies. In this paper, the GP is employed to seek the optimal hyper-parameters for the ML model using the RF algorithm, leading to superior predictive accuracy, as evaluated by the root mean square error (RMSE) of 0.0056. The advantage of this method is that antenna designers can directly use it to design antennas at any desired frequency within the 0.5-6 GHz range with high accuracy. The proposed method offers a transformative approach to antenna design by significantly reducing the optimization time by up to 99%, thereby improving the overall antenna design process.