• Türkçe
    • English
  • English 
    • Türkçe
    • English
  • Login
View Item 
  •   DSpace Home
  • ARAŞTIRMA ÇIKTILARI
  • Scopus İndeksli Yayınlar Koleksiyonu
  • View Item
  •   DSpace Home
  • ARAŞTIRMA ÇIKTILARI
  • Scopus İndeksli Yayınlar Koleksiyonu
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Nonlinear Short-term Prediction of Aluminum Foil Thickness via Global Regressor Combination

Thumbnail
View/Open
Nonlinear Short term Prediction of Aluminum Foil Thickness via Global Regressor Combination.pdf (3.518Mb)
Date
2017
Author
ÖZTÜRK, Ali
ŞEHERLİ, Rıfat
Metadata
Show full item record
Abstract
In this study, short-term prediction of aluminum foil thickness time-series data recorded during cold-rolling process was investigated. The locally projective nonlinear noise reduction was applied in order to improve the predictability of the time series. The higher-order statistics methods (bispectrum and bicoherence) were used to detect the nonlinearity. The embedding vectors with appropriate embedding dimension and time delay were obtained via the false nearest neighbors and mutual information methods, respectively. The maximum prediction horizon was determined depending on the maximal Lyapunov exponent. For various prediction horizons, the embedding vector and corresponding thickness value pairs were used as the dataset to assess the prediction performance of various machine learning algorithms (i.e., multilayer perceptron neural network, support vector machines with Pearson VII function-based kernel, and radial basis function network). The n-step ahead prediction outputs of the machine learning algorithms were globally combined with simple voting in favor of the one having minimum absolute error. The accuracy of our proposed method was compared with nonlinear autoregressive exogenous model for various thickness time-series data using mean absolute percentage error measure. © 2017 Taylor & Francis.
URI
http://hdl.handle.net/20.500.12498/2933
Collections
  • Scopus İndeksli Yayınlar Koleksiyonu [527]





Creative Commons License
DSpace@Karatay by Karatay University Institutional Repository is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License..

DSpace software copyright © 2002-2022  LYRASIS
Contact Us | Send Feedback
Theme by 
Atmire NV
 

 



| Yönerge | Rehber | İletişim |

sherpa/romeo

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsxmlui.ArtifactBrowser.Navigation.browse_typeThis CollectionBy Issue DateAuthorsTitlesSubjectsxmlui.ArtifactBrowser.Navigation.browse_type

My Account

LoginRegister

DSpace software copyright © 2002-2022  LYRASIS
Contact Us | Send Feedback
Theme by 
Atmire NV