Efficient Resonant Frequency Modeling for Dual-Band Microstrip Antennas by Gaussian Process Regression

Efficient Resonant Frequency Modeling
for Dual-Band Microstrip Antennas by
GaussianProcessRegression

Abstract— Efficient Resonant Frequency Modeling for Dual-Band Microstrip Antennas by Gaussian Process Regression. A methodology based on Gaussian process regression (GPR) for accurately modeling the resonant frequencies of dual-band microstrip antennas is presented. Two kinds of dual-band antennas were considered, namely a U-slot patch and a patch with a center square slot. Predictive results of high accuracy were achieved (normalized root-mean-square errors of below 0.6% in all cases), even for the square-slot patch modeling problem where all antenna dimensions and parameters were allowed to vary, resulting in a seven-dimensional input space. Training data requirements for achieving these accuracies were relatively modest. Furthermore, the automatic relevance determination property of GPR provided < Final Year Projects 2016 > at no additional cost a mechanism for enhancing qualitative understanding of the antennas’ resonance characteristics—a facility not offered by neural network-based strategies used in related studies.