Data Mining Techniques in Artificial Neural Network for UWB Antenna Design

dc.contributor.authorXiao, Li-Ye
dc.contributor.authorShao, Wei
dc.contributor.authorYao, Zhi-Xin
dc.contributor.authorGao, Shanshan
dc.coverage.issue1cs
dc.coverage.volume27cs
dc.date.accessioned2018-06-18T10:30:18Z
dc.date.available2018-06-18T10:30:18Z
dc.date.issued2018-04cs
dc.description.abstractWith data mining techniques for the preprocessing of training patterns, an artificial neural network (ANN) model is proposed for parametric modeling of electromagnetic behavior of ultrawide band (UWB) antennas in this paper. In this ANN method, two data mining techniques, including correlation analysis and data classification based on support vector machine (SVM), are employed to determine geometrical variable inputs and classify the inputs during the training and testing processes. Compared with the traditional ANN, the proposed model with data mining can achieve the trained model with small training datasets and accurate results. The validity and efficiency of this proposed method are confirmed with two band-notched UWB antenna examples.en
dc.formattextcs
dc.format.extent70-78cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2018 vol. 27, č. 1, s. 70-78. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2018.0070en
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/82962
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2018/18_01_0070_0078.pdfcs
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectArtificial neural network (ANN)en
dc.subjectdata miningen
dc.subjectpole-residue-based transfer function (TF)en
dc.subjectsupport vector machine (SVM)en
dc.subjectultrawide band (UWB) antennaen
dc.titleData Mining Techniques in Artificial Neural Network for UWB Antenna Designen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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