Abstract
In order to overcome the long time delays and dynamic complexity in industrial sintering process, a modeling method of prediction of burn-through point (BTP) was proposed based on support vector machines (SVMs). The results indicate SVMs outperform the three-layer Backpropagation (BP) neural network in predicting burn-through point with better generalization performance, and are satisfactory. The model can be used as plant model for the burn-through point control of on-strand sinter machines.
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References
Hu, J. Q., Rose, E.: Simulation of an Iiron Ore Sintering Plant. Research Report No.488. Department of Automatic Control and Systems Engineering, University of Sheffield, England (1993)
Young, R. W.: Dynamic Model of Sintering Process. Ironmaking and Steelmaking, 5 (1979) 25–31
Rose, E., Anderson, W. R. M., Orak, M.: Simulation of Sintering, IFAC World Congr., 10, (1993) 289–194
Augustin, M., Arbeithuber, C., JORGL, H. P.: Modeling and Simulation of an Iron Ore Sinter Strand. Proc. EUROSIM Congress’ 95, Sept. 11–15, Vienna, Austria (1995)
Vapnik, V. N.: The Nature of Statistical Leaning Theory. Springer-Verlag, New York (1995)
Vapnik, V. N.: Statistical Learning Theory. John Wiley & Sons, New York (1998)
Schölkopf, B.: Learning with Kernels. Ph. D. Thesis. Universität Tübingen, (1997)
Smola, A. J., Schölkopf, B.: A Tutorial on Support Vector Regression. NeuroCOLT2 Technical Report Series. UK: University of London, London (1998)
Müller, K. R., Smola, A. J., Rätsh, G.: Using Support Vector Machines for Time Series Prediction. Advances in Kernel Methods. Cambridge, MIT Press, MA (1998) 185–208
Platt, J. C.: Fast Training of SVMs Using Sequential Minimal Optimization. Schölkopf, B., Burges, C. J. C, Smola, A. J. Advances in Kernel Methods-Support Vector Learning. Cambridge, MIT Press, MA (1998) 185–208
Foresee, F. D., Hagan, M. T.: Gauss-Newton Approximation to Bayesian Regularization. Proceedings of the International Joint Conference on Neural Networks, Canada (1997)
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Wu, X., Fei, M., Wang, H., Zheng, S. (2006). Prediction of Sinter Burn-Through Point Based on Support Vector Machines. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Control and Automation. Lecture Notes in Control and Information Sciences, vol 344. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37256-1_88
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DOI: https://doi.org/10.1007/978-3-540-37256-1_88
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