Abstract:
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 (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.
