An accurate computation method based on artificial neural networks with different learning algorithms for resonant frequency of annular ring microstrip antennas

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dc.contributor.authorAkdağlı, Ali
dc.contributor.authorKayabaşı, Ahmet
dc.date.accessioned2020-03-26T18:49:20Z
dc.date.available2020-03-26T18:49:20Z
dc.date.issued2014
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractAn annular ring compact microstrip antenna (ARCMA) constructed by loading a circular slot in the center of the circular patch antenna is a popular microstrip antenna due to its favorable properties. In this study, a method based artificial neural networks (ANNs) has been firstly applied for the computing the resonant frequency of ARCMAs. Multilayered perceptron model based on feed forward back propagation ANN has been utilized, and the constructed model have been separately trained with 8 different learning algorithms to achieve the best results regarding the resonant frequency of ARCMAs at dominant mode. To this end, the resonant frequencies of 80 ARCMAs with varied dimensions and electrical parameters in accordance with UHF band covering GSM, LTE, WLAN and WiMAX applications were simulated with a robust numerical electromagnetic computational tool, IE3D (TM), which is based on method of moment. Then, ANN model was constructed with the simulation data, by using 70 ARCMAs for training and the remaining 10 for test. As the performances of the 8 learning algorithms are compared with each other, the best result is obtained with Levenberg-Marquardt algorithm. The proposed ANN model were confirmed by comparing with the suggestions reported elsewhere via measurement data published earlier in the literature, and they have further validated on an ARCMA fabricated in this study. The results achieved in this study show that ANN model learning with LM algorithm can be successfully used to compute the resonant frequency of ARCMAs without involving any sophisticated methods.en_US
dc.identifier.doi10.1007/s10825-014-0624-6en_US
dc.identifier.endpage1019en_US
dc.identifier.issn1569-8025en_US
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1014en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s10825-014-0624-6
dc.identifier.urihttps://hdl.handle.net/20.500.12395/30585
dc.identifier.volume13en_US
dc.identifier.wosWOS:000344607500028en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofJOURNAL OF COMPUTATIONAL ELECTRONICSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectAnnular ring compact microstrip antennaen_US
dc.subjectResonant frequencyen_US
dc.subjectArtificial neural network (ANN)en_US
dc.subjectLearning algorithmsen_US
dc.subjectLevenberg-Marquardt algorithmen_US
dc.titleAn accurate computation method based on artificial neural networks with different learning algorithms for resonant frequency of annular ring microstrip antennasen_US
dc.typeArticleen_US

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