Artificial neural network application for modeling the rail rolling process

Author:

Altınkaya Hüseyin,Orak İlhami M.,Esen İsmail

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference27 articles.

1. Neural, fuzzy and grey-box modelling for entry temperature prediction in a hot strip mill;Barrios;Expert Systems with Applications,2012

2. Beale, M. H., Hagan, M. T., & Demuth, H. B. (2013). Neural network toolbox user’s guide. MathWorks, Technical Support.

3. Prediction of performance and exhaust emissions of a diesel engine fuelled with biodiesel produced from waste frying palm oil;Canakci;Expert Systems with Applications,2009

4. Cser, L., Korhonen, A. S., Gulyas, J. Mantyla, P., Simula, O., & Reiss, Gy., et al. (1999). Data mining and state monitoring in hot rolling. Bay Zoltan Institute for Logistics and Production Systems, H3519, Miskolc-Tapolca, Hungry.

5. Guerrero, M. A., Belzunce, J., & Jorge, J. (2009). Hot rolling process simulation. Application to UIC-60 rail rolling. In Proceedings of the 4th IASME/WSEAS international conference on continuum mechanics (CM’09).

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