Analysis and synthesis of L- and T-shaped flexible compact microstrip antennas using regression-based machine learning approaches

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dc.authoridBICER, Mustafa Berkan/0000-0003-3278-6071
dc.contributor.authorBicer, Mustafa Berkan
dc.date.accessioned2025-03-17T12:25:28Z
dc.date.available2025-03-17T12:25:28Z
dc.date.issued2023
dc.departmentTarsus Üniversitesi
dc.description.abstractThe purpose of this study was to analyze and synthesize L-shaped compact microstrip antennas (LCMA) and T-shaped compact microstrip antennas using regression-based machine learning algorithms (TCMA). This was accomplished by simulating 3808 LCMAs and 900 TCMAs operating at UHF and SHF frequencies with different physical and electrical characteristics. The acquired data was utilized to create a data set containing the antennas' physical and electrical characteristics, as well as their resonant frequencies in the TM010 mode. Four baseline regression models and seven machine learning models were developed to determine the resonance frequency of antennas and the values of the physical parameters required for a particular frequency. To examine the efficacy of machine learning models, three-dimensional LCMAs and TCMAs were created using polylactic acid (PLA) and felt-based flexible substrates, as well as copper tape. The results illustrate the feasibility of using machine learning models for LCMA and TCMA analysis and synthesis.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [122E093]
dc.description.sponsorshipThis study is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) with project number of 122E093.
dc.identifier.doi10.1515/freq-2022-0070
dc.identifier.endpage292
dc.identifier.issn0016-1136
dc.identifier.issn2191-6349
dc.identifier.issue5-6
dc.identifier.scopus2-s2.0-85142417296
dc.identifier.scopusqualityQ3
dc.identifier.startpage281
dc.identifier.urihttps://doi.org/10.1515/freq-2022-0070
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1701
dc.identifier.volume77
dc.identifier.wosWOS:000884459200001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBicer, Mustafa Berkan
dc.language.isoen
dc.publisherWalter De Gruyter Gmbh
dc.relation.ispartofFrequenz
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectflexible antennas
dc.subjectLCMA
dc.subjectmachine learning
dc.subjectmicrostrip antennas
dc.subjectregression analysis
dc.subjectTCMA
dc.titleAnalysis and synthesis of L- and T-shaped flexible compact microstrip antennas using regression-based machine learning approaches
dc.typeArticle

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