Çarpmasız yapay sinir ağı
buir.contributor.author | Çetin, A. Enis | |
buir.contributor.orcid | Çetin, A. Enis|0000-0002-3449-1958 | |
dc.citation.epage | 2418 | en_US |
dc.citation.spage | 2416 | en_US |
dc.contributor.author | Akbaş, Cem Emre | en_US |
dc.contributor.author | Bozkurt, Alican | en_US |
dc.contributor.author | Çetin, A. Enis | en_US |
dc.contributor.author | Çetin-Atalay, R. | en_US |
dc.contributor.author | Üner, A. | en_US |
dc.coverage.spatial | Malatya, Turkey | |
dc.date.accessioned | 2016-02-08T12:23:04Z | |
dc.date.available | 2016-02-08T12:23:04Z | |
dc.date.issued | 2015-05 | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description | Date of Conference: 16-19 May 2015 | |
dc.description | Conference name: 23nd Signal Processing and Communications Applications Conference, SIU 2015 | |
dc.description.abstract | Bu 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.abstract | In 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.description.provenance | Made available in DSpace on 2016-02-08T12:23:04Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2015 | en |
dc.identifier.doi | 10.1109/SIU.2015.7130369 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/28530 | |
dc.language.iso | Turkish | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/SIU.2015.7130369 | en_US |
dc.source.title | 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings | en_US |
dc.subject | Artificial Neural Network | en_US |
dc.subject | Multiplication-free Operator | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Inner product | en_US |
dc.subject | Input vector | en_US |
dc.subject | LMS algorithms | en_US |
dc.subject | Low Power | en_US |
dc.subject | Multiplication-free Operator | en_US |
dc.subject | Processing power | en_US |
dc.subject | Vector operators | en_US |
dc.subject | Signal processing | en_US |
dc.title | Çarpmasız yapay sinir ağı | en_US |
dc.title.alternative | Multiplication-free neural networks | en_US |
dc.type | Conference Paper | en_US |
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