Jacobs, Jan Pieter2015-12-022015-12-022015Jacobs, JP 2015, 'Efficient resonant frequency modeling for dual-band microstrip antennas by Gaussian process regression', IEEE Antennas and Wireless Propagation Letters, vol. 14, pp. 337-341.1536-1225 (print)1548-5757 (online)10.1109/LAWP.2014.2362937http://hdl.handle.net/2263/51013A 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.en© 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.ModelingGaussian process regression (GPR)Resonant frequenciesDual-band microstrip antennasPostprint Article