Çarpmasız yapay sinir ağı

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage2418en_US
dc.citation.spage2416en_US
dc.contributor.authorAkbaş, Cem Emreen_US
dc.contributor.authorBozkurt, Alicanen_US
dc.contributor.authorÇetin, A. Enisen_US
dc.contributor.authorÇetin-Atalay, R.en_US
dc.contributor.authorÜner, A.en_US
dc.coverage.spatialMalatya, Turkey
dc.date.accessioned2016-02-08T12:23:04Z
dc.date.available2016-02-08T12:23:04Z
dc.date.issued2015-05en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 16-19 May 2015
dc.descriptionConference name: 23nd Signal Processing and Communications Applications Conference, SIU 2015
dc.description.abstractBu bildiride çarpma işlemi kullanmadan oluşturulan bir Yapay Sinir Ağı (YSA) sunulmaktadır. Girdi vektörleri ve YSA katsayılarının iç çarpımları çarpmasız bir vektör işlemiyle hesaplanmıştır. Yapay sinir ağının eğitimi sign-LMS algoritması ile yapılmıştır. Önerilen YSA sistemi, hesap gücü kısıtlı olan veya düşük enerji tüketimine ihtiyaç duyulan mikroişlemcilerde kullanılabilir.
dc.description.abstractIn this article, a multiplication-free artificial Neural Network (ANN) structure is proposed. Inner products between the input vectors and the ANN weights are implemented using a multiplication-free vector operator. Training of the new artificial neural network structure is carried out using the sign-LMS algorithm. Proposed ANN system can be used in applications requiring low-power usage or running on microprocessors that have limited processing power. © 2015 IEEE.
dc.identifier.doi10.1109/SIU.2015.7130369en_US
dc.identifier.urihttp://hdl.handle.net/11693/28530
dc.language.isoTurkishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2015.7130369en_US
dc.source.title23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedingsen_US
dc.subjectArtificial Neural Networken_US
dc.subjectMultiplication-free Operatoren_US
dc.subjectNeural networksen_US
dc.subjectInner producten_US
dc.subjectInput vectoren_US
dc.subjectLMS algorithmsen_US
dc.subjectLow Poweren_US
dc.subjectMultiplication-free Operatoren_US
dc.subjectProcessing poweren_US
dc.subjectVector operatorsen_US
dc.subjectSignal processingen_US
dc.titleÇarpmasız yapay sinir ağıen_US
dc.title.alternativeMultiplication-free neural networksen_US
dc.typeConference Paperen_US

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