A Hybrid Model For The Prediction Of Aluminum Foil Output Thickness İn Cold Rolling Process

This study proposes a hybrid model composed of multiple prediction algorithms and an autoregressive moving average (ARMA) module for the thickness prediction. In order to attain higher accuracy, the prediction algorithms were globally combined by simple voting to reduce the effect of the inductive bias imposed by each algorithm on the dataset. The global multiexpert combination (GMEC) system included the multilayer perceptron neural network (MLPNN), radial basis function network (RBFN), multiple linear regression (MLR), and support vector machines (SVM) algorithms. The proposed hybrid model extends the GMEC system by integrating an ARMA module for the output. On the test dataset, the mean absolute error (MEA) and root mean squared error (RMSE) were better for the hybrid model than the GMEC system. The GMEC system had approximately twice better performance than the MLPNN, which was the best among the learners. The performance was significantly improved via the hybrid model in terms of correlation coefficient (R). The results suggested that the proposed hybrid model can be used for more accurate and precise prediction of aluminum foil output thickness. © TÜBİTAK

  • Koleksiyonlar
Erişime Açık
Görüntülenme
2
22.03.2024 tarihinden bu yana
İndirme
1
22.03.2024 tarihinden bu yana
Son Erişim Tarihi
18 Mayıs 2024 13:34
Google Kontrol
Tıklayınız
Tam Metin
Tam Metin İndirmek için tıklayın Ön izleme
Detaylı Görünüm
Eser Adı
(dc.title)
A Hybrid Model For The Prediction Of Aluminum Foil Output Thickness İn Cold Rolling Process
Yayın Türü
(dc.type)
Makale
Yazar/lar
(dc.contributor.author)
ÖZTÜRK, Ali
Yazar/lar
(dc.contributor.author)
ŞEHERLİ, Rıfat
DOI Numarası
(dc.identifier.doi)
10.3906/elk-1803-25
Atıf Dizini
(dc.source.database)
Scopus
Konu Başlıkları
(dc.subject)
Prediction
Konu Başlıkları
(dc.subject)
Aluminum Foil
Konu Başlıkları
(dc.subject)
Autoregressive Moving Average
Konu Başlıkları
(dc.subject)
Global Expert Combination
Yayıncı
(dc.publisher)
Turkiye Klinikleri
Yayın Tarihi
(dc.date.issued)
2019
Kayıt Giriş Tarihi
(dc.date.accessioned)
2020-08-07T12:51:45Z
Açık Erişim tarihi
(dc.date.available)
2020-08-07T12:51:45Z
Kaynak
(dc.source)
Turkish Journal of Electrical Engineering and Computer Sciences
ISSN
(dc.identifier.issn)
13000632 (ISSN)
Özet
(dc.description.abstract)
This study proposes a hybrid model composed of multiple prediction algorithms and an autoregressive moving average (ARMA) module for the thickness prediction. In order to attain higher accuracy, the prediction algorithms were globally combined by simple voting to reduce the effect of the inductive bias imposed by each algorithm on the dataset. The global multiexpert combination (GMEC) system included the multilayer perceptron neural network (MLPNN), radial basis function network (RBFN), multiple linear regression (MLR), and support vector machines (SVM) algorithms. The proposed hybrid model extends the GMEC system by integrating an ARMA module for the output. On the test dataset, the mean absolute error (MEA) and root mean squared error (RMSE) were better for the hybrid model than the GMEC system. The GMEC system had approximately twice better performance than the MLPNN, which was the best among the learners. The performance was significantly improved via the hybrid model in terms of correlation coefficient (R). The results suggested that the proposed hybrid model can be used for more accurate and precise prediction of aluminum foil output thickness. © TÜBİTAK
Yayın Dili
(dc.language.iso)
en
Tek Biçim Adres
(dc.identifier.uri)
http://hdl.handle.net/20.500.12498/2816
Analizler
Yayın Görüntülenme
Yayın Görüntülenme
Erişilen ülkeler
Erişilen şehirler
6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve cerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.

creativecommons
Bu site altında yer alan tüm kaynaklar Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.
Platforms