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CLC number: TP391

On-line Access: 2012-12-09

Received: 2012-05-24

Revision Accepted: 2012-09-20

Crosschecked: 2012-11-12

Cited: 3

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.12 P.881-890

10.1631/jzus.C1200156


Adaptive online prediction method based on LS-SVR and its application in an electronic system


Author(s):  Yang-ming Guo, Cong-bao Ran, Xiao-lei Li, Jie-zhong Ma

Affiliation(s):  School of Computer Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

Corresponding email(s):   yangming_g@nwpu.edu.cn

Key Words:  Adaptive online prediction, Least squares support vector regression (LS-SVR), Electronic system


Yang-ming Guo, Cong-bao Ran, Xiao-lei Li, Jie-zhong Ma. Adaptive online prediction method based on LS-SVR and its application in an electronic system[J]. Journal of Zhejiang University Science C, 2012, 13(12): 881-890.

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author="Yang-ming Guo, Cong-bao Ran, Xiao-lei Li, Jie-zhong Ma",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1200156"
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%T Adaptive online prediction method based on LS-SVR and its application in an electronic system
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%A Cong-bao Ran
%A Xiao-lei Li
%A Jie-zhong Ma
%J Journal of Zhejiang University SCIENCE C
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%N 12
%P 881-890
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1200156

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T1 - Adaptive online prediction method based on LS-SVR and its application in an electronic system
A1 - Yang-ming Guo
A1 - Cong-bao Ran
A1 - Xiao-lei Li
A1 - Jie-zhong Ma
J0 - Journal of Zhejiang University Science C
VL - 13
IS - 12
SP - 881
EP - 890
%@ 1869-1951
Y1 - 2012
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1200156


Abstract: 
Health trend prediction has become an effective way to ensure the safe operation of highly reliable systems, and online prediction is always necessary in many real applications. To simultaneously obtain better or acceptable online prediction accuracy and shorter computing time, we propose a new adaptive online method based on least squares support vector regression (LS-SVR). This method adopts two approaches. One approach is that we delete certain support vectors by judging the linear correlation among the samples to increase the sparseness of the prediction model. This approach can control the loss of useful information in sample data, improve the generalization capability of the prediction model, and reduce the prediction time. The other approach is that we reduce the number of traditional LS-SVR parameters and establish a modified simple prediction model. This approach can reduce the calculation time in the process of adaptive online training. Simulation and a certain electric system application indicate preliminarily that the proposed method is an effective prediction approach for its good prediction accuracy and low computing time.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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