Abstract
The rolling force model for cold tandem mill was put forward by using the Elman dynamic recursive network method, based on the actual measured data. Furthermore, a good assumption is put forward, which brings a full universe of discourse self-adjusting factor fuzzy control, closed-loop adjusting, based on error feedback and expertise into a rolling force prediction model, to modify prediction outputs and improve prediction precision and robustness. The simulated results indicate that the method is highly effective and the prediction precision is better than that of the traditional method. Predicted relative error is less than ±4%, so the prediction is high precise for the cold tandem mill.
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References
Draeger A, Engell S, Ranke H. Model Predictive Control Using Neural Networks [J]. IEEE Control systems, 1995, 15(5): 61.
Kumpati S N, Kannan P. Identification and Control of Dynamical Systems Using Neural Networks [J]. IEEE Trans on Neural Networks, 1990, 1(1): 4.
Nicklaus F P, Dieter L, Gunter S. Application of Neural Networks in Rolling Mill Automation [J]. Iron and Steel Engineer, 1995, 72(2): 33.
Ishii T, Wada S, Miyokawa M, et al. Recent Technology in Hot Strip Mill [A]. Kiuchi M, eds. Proceedings of the 7th International Conference on Steel Rolling [C]. Chiba: the Iron and Steel Institute of Japan, 1998. 711.
Simens A G. Intelligent Answer to HSM Control Problems [J]. Steel Times International, 1996, 20(1): 16.
Nicolau V, Aiordachioaie D, Popa R. Neural Network Prediction of the Wave Influence on the Yaw Motion of a Ship [A]. 2004 IEEE International Joint Conference on Neural Networks [C]. Budapest: 2004. 2801.
Panella M, Rizzi A, Martinelli G. Refining Accuracy of Environmental Data Prediction by MoG Neural Networks [J]. Neurocomputing, 2003, 55(3-4): 521.
Ganjefar S, Janabi-Sharifi F, Hosseinipanah S M A, et al. Prediction of Delay Time in Internet by Neural Network [A]. 2005 IEEE International Conference on Control Applications [C]. Toronto; 2005. 340.
Kieltyka L, Kuceba R, Sokolowski A. Application of Neural Network Topologies in the Intelligent Heat Use Prediction System [A]. Artificial Intelligence and Soft Computing-ICAISC 2004 Proceedings [C]. Zakopane, Poland: 2004. 1146.
WANG Chang-hong, XU U-xin. A Local Recurrent Neural Network Model and Its Application for Identification of Dynamic System [J]. Journal of Harbin Institute of Technology, 1998, 30(4): 21 (in Chinese).
SUN Zeng-yin, ZHANG Zai-xing, DENG Zhi-dong. Intelligent Control Theory and Technology [M]. Beijng: Tsinghua University Publishing Company, 1997 (in Chinese).
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Foundation Item: Item Sponsored by Natural Science Foundation of Hebei Province of China (E2004000206); National Natural Science Foundation of China (50675186)
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Jia, Cy., Shan, Xy. & Niu, Zp. High Precision Prediction of Rolling Force Based on Fuzzy and Nerve Method for Cold Tandem Mill. J. Iron Steel Res. Int. 15, 23–27 (2008). https://doi.org/10.1016/S1006-706X(08)60025-4
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DOI: https://doi.org/10.1016/S1006-706X(08)60025-4