Resonant Frequency Modelling of Microstrip Antennas by Consensus Network and Student’s-T Process

Abstract

When modelling and optimizing antennas by machine learning (ML) methods, it is the most time-consuming to obtain the training samples with labels from full-wave electromagnetic simulation software. To address the problem, this paper proposes an optimization method based on the consensus results of multiple independently trained Student’s-T Process (STP) with excellent generalization ability. First, the STP is introduced as a surrogate model to replace the traditional Gaussian Process (GP), and the hyperparameters of the STP model are optimized. Afterwards, a consistency algorithm is used to process the results of multiple independently trained STPs to improve the reliability of the results. Furthermore, an aggregation algorithm is adopted to reduce the error obtained in the consistency results if it is greater than the consistency flag. The effectiveness of the proposed model is demonstrated through experiments with rectangular microstrip antennas (RMSA) and circular microstrip antennas (CMSA). The experimental results show that the use of multiple independently trained STPs can accelerate the antenna design optimization process, and improve modelling accuracy while maintaining modelling efficiency, which has high generalization ability.