Research Article Open Access

Hybrid Hot Strip Rolling Force Prediction using a Bayesian Trained Artificial Neural Network and Analytical Models

Abdelkrim Moussaoui, Yacine Selaimia and Hadj A. Abbassi

Abstract

The authors discuss the combination of an Artificial Neural Network (ANN) with analytical models to improve the performance of the prediction model of finishing rolling force in hot strip rolling mill process. The suggested model was implemented using Bayesian Evidence based training algorithm. It was found that the Bayesian Evidence based approach provided a superior and smoother fit to the real rolling mill data. Completely independent set of real rolling data were used to evaluate the capacity of the fitted ANN model to predict the unseen regions of data. As a result, test rolls obtained by the suggested hybrid model have shown high prediction quality comparatively to the usual empirical prediction models.

American Journal of Applied Sciences
Volume 3 No. 6, 2006, 1885-1889

DOI: https://doi.org/10.3844/ajassp.2006.1885.1889

Submitted On: 30 May 2006 Published On: 30 June 2006

How to Cite: Moussaoui, A., Selaimia, Y. & Abbassi, H. A. (2006). Hybrid Hot Strip Rolling Force Prediction using a Bayesian Trained Artificial Neural Network and Analytical Models . American Journal of Applied Sciences, 3(6), 1885-1889. https://doi.org/10.3844/ajassp.2006.1885.1889

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Keywords

  • Hot rolling mill
  • neural networks modelling
  • bayesian evidence