Gaussian process modeling of CPW-FED slot antennas

dc.contributor.authorDe Villiers, Johan Pieter
dc.contributor.authorJacobs, Jan Pieter
dc.contributor.emailpieter.devilliers@up.ac.zaen
dc.date.accessioned2010-04-08T06:27:17Z
dc.date.available2010-04-08T06:27:17Z
dc.date.issued2009
dc.description.abstractGaussian 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 second-resonant 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 analysis.en
dc.identifier.citationDe Villiers, JP & Jacobs, JP 2009, 'Gaussian process modeling of CPW-FED slot antennas', Progress In Electromagnetics Research, vol. 98, pp. 233-249. [http://ceta.mit.edu/PIER/]en
dc.identifier.issn1070-4698
dc.identifier.urihttp://hdl.handle.net/2263/13849
dc.language.isoenen
dc.publisherEMW Publishingen
dc.rightsEMW Publishingen
dc.subject.lcshGaussian processesen
dc.subject.lcshDistribution (Probability theory)en
dc.subject.lcshRegression analysisen
dc.subject.lcshNeural networks (Computer science)en
dc.subject.lcshSlot antennasen
dc.subject.lcshStochastic processesen
dc.subject.lcshAntennas, Dipoleen
dc.titleGaussian process modeling of CPW-FED slot antennasen
dc.typeArticleen

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