Trend Prediction of Hydraulic Liquid Leakage Based on ES-LSSVM

Article Preview

Abstract:

A novel prediction method which combined evolutionary strategy with least-square support vector machine is presented and applied to the trend prediction of hydraulic liquid leakage in this paper. In order to improve the prediction performance, the evolutionary strategy is employed to optimize the internal parameters of least-square support vector machine. Through the experiment study, the result validated the effectiveness of the prediction method, and it is also demonstrated that the method is able to do the short-term fault prediction for the hydraulic system.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

440-444

Citation:

Online since:

October 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] SUYKENS JAK, VANDEWALLE J. Least squares support vectors machine classifiers, Neural Processing Letters, 9(1999) 293-300.

Google Scholar

[2] LEE Y J, HSIEH W F, HUANG C M. ε-SSVR: A smooth support vector machine forε-insensitive regression, IEEE Transactions on Knowledge and Data Engineering, 17(2005): 678-685.

DOI: 10.1109/tkde.2005.77

Google Scholar

[3] Wang Keqi, Yang Shaochun, Method of optimizing parameter of least squares support vector machines by genetic algorithm. Computer Applications and Software, 26(2009) 109-111.

Google Scholar

[4] Li Dazhong, Han Pu and Wang Zhen, The biomass gasification process modeling and optimization based on SVM and PSO. Journal of North China Electric Power University, 36(2009) 74-79.

Google Scholar

[5] T. Bck. Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York, (1996).

Google Scholar