A Study of the Screening Efficiency of a Probability Sieve Based on Higher-Order Spectrum Analysis and Support Vector Machines


Article Preview

Aiming at drawbacks of current methods for predicting the screening efficiency of probability sieve, this paper proposed a method of predict and study the screening efficiency of probability sieve based on higher-order spectrum(HOS) analysis and support vector machines(SVMs). First setting up trispectrum model with the vibration signals, then fitting out polynomial with least square method using the data which get out by the reconstruct power spectrum. Finaly, using support vector machines to predicting the screening efficiency with the coefficient of the polynomial as the sample input. The results show that the relative errors are all less than 2.4% and the absolute errors are all less than 0.021, which is ideal for efficiency forecast.



Advanced Materials Research (Volumes 291-294)

Edited by:

Yungang Li, Pengcheng Wang, Liqun Ai, Xiaoming Sang and Jinglong Bu






Z. Z. Shi and Y. J. Huang, "A Study of the Screening Efficiency of a Probability Sieve Based on Higher-Order Spectrum Analysis and Support Vector Machines", Advanced Materials Research, Vols. 291-294, pp. 2089-2093, 2011

Online since:

July 2011




[1] JIAO Hong-guang, Zhao Yue-min, Luo Zhen-fu. Journal of China University of Mining & Technology, 2006: 384~388. (in Chinese).

[2] LI Zhen-liang, MEN Wu-bin. Sea-Lake Salt and Chemical Industry,2005,34(2): 22-26. (in Chinese).

[3] JIAO Lin-de,CUI Shu-zhi. Sea-Lake Salt and Chemical Industry,2001,30(4):34-35. (in Chinese).

[4] ZHAO Yue-min,LIU Chu-sheng. Dry screening theory and Its Application. Beijing:Science Press,1999: 1-154. (in Chinese).

[5] ZHANG Xian-da. Time series analysis. Beijing: Tsinghua Univ Press, 1999: 11−13. (in Chinese).

[6] W B Collis,P R White, J K Hammond. Mechanical Systems and Signal Processing, 1998, 12(3): 375-394.

[7] Hyungjun Park, Joo-Haeng Lee. Computer-Aided Design, 2007, 39.

[8] Hiroyuki Kano, Hiroaki Nakata, and Clyde F. Martin. Applied Mathematics and Computation on, 2005, 169.

[9] Cherkassky V, Ma Y. Neural Networks, 2004, 17(1): 1132126.

[10] Vapnik V N. The Nature of Statistical Learning Theory, NY: Springer-Verlag, (1995).

[11] Schlkopf B, Smola A J. Learning with Kernels. Cambridge: MIT Press, (2002).

In order to see related information, you need to Login.