Randomized feed-forward artificial neural networks in estimating short-term power load of a small house: a case study

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dc.authoridTekin, Ramazan/0000-0003-4325-6922
dc.contributor.authorErtugrul, Omer Faruk
dc.contributor.authorTekin, Ramazan
dc.contributor.authorKaya, Yilmaz
dc.date.accessioned2024-12-24T19:23:55Z
dc.date.available2024-12-24T19:23:55Z
dc.date.issued2017
dc.departmentSiirt Üniversitesi
dc.description2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY
dc.description.abstractRandomized 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.
dc.description.sponsorshipIEEE Turkey Sect,Anatolian Sci
dc.identifier.isbn978-1-5386-1880-6
dc.identifier.scopus2-s2.0-85039917875
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12604/5744
dc.identifier.wosWOS:000426868700184
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2017 International Artificial Intelligence and Data Processing Symposium (Idap)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectRandom weight neural network
dc.subjectrandom vector functional link neural network
dc.subjectextreme learning machine
dc.subjectradial bases function neural network
dc.subjectshort-term power load
dc.titleRandomized feed-forward artificial neural networks in estimating short-term power load of a small house: a case study
dc.typeConference Object

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