Randomized feed-forward artificial neural networks in estimating short-term power load of a small house: A case study
| dc.authorid | 0000-0003-0710-0867 | en_US |
| dc.authorid | 0000-0003-4325-6922 | en_US |
| dc.authorid | 0000-0002-0841-6399 | en_US |
| dc.contributor.author | Ertuğrul, Ömer Faruk | |
| dc.contributor.author | Tekin, Ramazan | |
| dc.contributor.author | Kaya, Yılmaz | |
| dc.date.accessioned | 2019-07-04T13:06:46Z | |
| dc.date.available | 2019-07-04T13:06:46Z | |
| dc.date.issued | 2017-11-02 | en_US |
| dc.department | Batman Üniversitesi Mühendislik - Mimarlık Fakültesi Elektrik-Elektronik Mühendisliği Bölümü | en_US |
| dc.department | Batman Üniversitesi Mühendislik - Mimarlık Fakültesi Bilgisayar Mühendisliği Bölümü | en_US |
| dc.description.abstract | Randomized feed-forward artificial neural networks (ANNs) have been employed in various domains. This paper was written in order to assess the efficiency of the basic forms of randomized feed-forward ANNs, which are randomized weight artificial neural network, random vector functional link network, extreme learning machine, and radial bases function neural network. In order to compare these methods, a complex dataset, which is the power load of a small house dataset, was used. Obtained results showed that lower training error rates were achieved by randomized vector functional link network. On the other hand, lower test error rates were achieved by ELM. Furthermore, ELM has faster training and test stages than the other employed randomized ANNs. | en_US |
| dc.identifier.citation | Ertuğrul, Ö. F., Tekin, R., Kaya, Y. (2017). Randomized feed-forward artificial neural networks in estimating short-term power load of a small house: A case study. 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 16-17 Sept. 2017, Malatya, Turkey. https://doi.org/10.1109/idap.2017.8090344 | en_US |
| dc.identifier.isbn | 978-1-5386-1880-6 | |
| dc.identifier.isbn | 978-1-5386-1881-3 | |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://doi.org/10.1109/idap.2017.8090344 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12402/2182 | |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.isversionof | 10.1109/idap.2017.8090344 | en_US |
| dc.relation.journal | 2017 International Artificial Intelligence and Data Processing Symposium (IDAP) | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
| dc.subject | Extreme Learning Machine | en_US |
| dc.subject | Radial Bases Function Neural Network | en_US |
| dc.subject | Random Vector Functional Link Neural Network | en_US |
| dc.subject | Random Weight Neural Network | en_US |
| dc.subject | Short-Term Power Load | en_US |
| dc.title | Randomized feed-forward artificial neural networks in estimating short-term power load of a small house: A case study | en_US |
| dc.type | Conference Object | en_US |
