Ertuğrul, Ömer FarukTekin, RamazanKaya, Yılmaz2019-07-042019-07-042017-11-02Ertuğrul, 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.2017.8090344978-1-5386-1880-6978-1-5386-1881-3https://doi.org/10.1109/idap.2017.8090344https://hdl.handle.net/20.500.12402/2182Randomized 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.eninfo:eu-repo/semantics/closedAccessAttribution-NonCommercial-ShareAlike 3.0 United StatesExtreme Learning MachineRadial Bases Function Neural NetworkRandom Vector Functional Link Neural NetworkRandom Weight Neural NetworkShort-Term Power LoadConference ObjectN/AN/A