Machine Learning-Based Prediction of Optimal Switch Configurations for Boost Converters by PowerSynth

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dc.contributor.authorOgunbiyi, Mobolaji
dc.date.accessioned2024-10-01T18:05:14Z
dc.date.available2024-10-01T18:05:14Z
dc.date.issued2024
dc.description.urihttps://scholarworks.uark.edu/cgi/viewcontent.cgi?article=1015&context=elegreu
dc.format.extent1 page
dc.genreposters
dc.identifierdoi:10.13016/m2bw0q-ty4e
dc.identifier.urihttp://hdl.handle.net/11603/36549
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectLayout Optimization
dc.subjectZero-Voltage Switching (ZVS)
dc.subjectMonte Carlo Optimization
dc.subjectBidirectional DC-DC Converter
dc.titleMachine Learning-Based Prediction of Optimal Switch Configurations for Boost Converters by PowerSynth
dc.typeText

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