Gaussian process (GP) regression is proposed as a structured supervised learning alternative to neural networks for the modeling of CPW-fed slot antenna input characteristics. A Gaussian process is a stochastic process and entails the generalization of the Gaussian probability distribution to functions. Standard GP regression is applied to modeling S11 against frequency of a CPW-fed secondresonant slot dipole, while an approximate method for large datasets is applied to an ultrawideband (UWB) slot with U-shaped tuning stub --- a challenging problem given the highly non-linear underlying function that maps tunable geometry variables and frequency to S11/input impedance. Predictions using large test data sets yielded results of an accuracy comparable to the target moment-method-based full-wave simulations, with normalized root mean squared errors of 0.50% for the slot dipole, and below 1.8% for the UWB antenna. The GP methodology has various inherent benefits, including the need to learn only a handful of (hyper) parameters, and training errors that are effectively zero for noise-free observations. GP regression would be eminently suitable for integration in antenna design algorithms as a fast substitute for computationally intensive full-wave analyses.
J. P. de Villiers,
Jan Pieter Jacobs,
"Gaussian Process Modeling of CPW-Fed Slot Antennas," Progress In Electromagnetics Research,
Vol. 98, 233-249, 2009. doi:10.2528/PIER09083103
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