Randomized feed-forward artificial neural networks in estimating short-term power load of a small house: A case study
Yükleniyor...
Dosyalar
Tarih
2017-11-02
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Attribution-NonCommercial-ShareAlike 3.0 United States
Attribution-NonCommercial-ShareAlike 3.0 United States
Özet
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.
Açıklama
Anahtar Kelimeler
Extreme Learning Machine, Radial Bases Function Neural Network, Random Vector Functional Link Neural Network, Random Weight Neural Network, Short-Term Power Load
Kaynak
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
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
