The Temperature Prediction in Blast Furnace Base on Fuzzy Least Squares Support Vector Machine

Abstract:

The temperature prediction in blast furnace loses accuracy or Forecasts failure when the temperatures change is at normal levels and obvious. This paper introduces fuzzy membership of samples basing on support vector data description and the fuzzy least squares support vector machine to forecast the blast furnace temperature. Then the simulation was done by using the forecast samples and the model after training by MATLAB. Comparing the simulation results of LS-FSVM with LS-SVM, the model basing on LS-FSVM enhances anti-jamming ability. The accuracy of the temperature prediction in blast furnace promotes significantly when the temperature of blast furnace fluctuates.

• Periodical: Applied Mechanics and Materials (Volumes 336-338)
• Pages: 566-569
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.336-338.566
• Online since: July 2013
• Authors: Qing Xin Zhang, Yong Tao, Zhan Bo Cui
• Keywords: Fuzzy Membership, LS-FSVM, LSSVM, Support Vector Data Description, Temperature Prediction

References

[1] Liu X Y, Liu Y G, Wang W H. Application of Bayesian Network to Predicting Silicon Content in Hot Metal[J]. Iron and Steel, 2005, 40(3):17-21.

[2] Radhakrishnan V R, Mohamed A R. Neural netwoks for the identification and control of blast furnace hot metal quality [J]. Journal of Process Control, 2000, 6 (10):509-524.

DOI: 10.1016/s0959-1524(99)00052-9

[3] Wu Q, Liu S Y, Du Z. Fuzzy least square support vector machines for regression[J], Journal of Xidian university, 2007, 34 (5):773-778

[4] Liu Y G, Wang W H. Application of Wavelet Analysis to Prediction of Silicon Content in Hot Metal[J]. Iron and Steel, 2005, 40 (8):15-17.

[5] Chen-Fu Lin, Sheng-De wang. Fuzzy Support Vector Machines [J]. IEEE Trans Neural Network, 2002, 13(2):464-471.

DOI: 10.1109/72.991432

[6] David M.J. Tax, Robert P.W. Duin. Support Vector Domain Description[J]. Pattern Recognition Letters, 20(1999): 1191-1199.

DOI: 10.1016/s0167-8655(99)00087-2

[7] Yuan X F, Wang Y N. On fuzzy support vector machine controller[J]. Control and Decision, 2005, 20(5): 537-540.

[8] Jian L. Applications of SVM to Predict Silicon Content in Hot Metal[D]. 2006.

[9] Marti Hearst. Support Vector Machines[J]. IEEE Transaction On Intelligent Systems.1998.8.

[10] Corinna Cortes, Vladimir Vapnik. Support vector networks[J]. Machine Learning, 1995,20(3): 273-295.

[11] Vapnik V N. The Nature of Statistical Learning Theory[M]. New York: Springer-Verlag, 1995.

[12] Vapnik V N. An overview of Statistical Learning Theory[J]. IEEE Trans Neural Network, 1995,10(5):988-999.

[13] Du Z. Research on Some Variants of Support Vector Machine[D]. 2009.

[14] Liu X Y, Li P, Hao C H. Fast Leave-One-Out Cross-Validation Algorithm for Extreme Learning Machine[J]. Journal of Shanghai jiaotong university, 2011, 45 (8):1140-1145.