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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (12): 881-890    DOI: 10.1631/jzus.C1200156
    
Adaptive online prediction method based on LS-SVR and its application in an electronic system
Yang-ming Guo, Cong-bao Ran, Xiao-lei Li, Jie-zhong Ma
School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
Adaptive online prediction method based on LS-SVR and its application in an electronic system
Yang-ming Guo, Cong-bao Ran, Xiao-lei Li, Jie-zhong Ma
School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
 全文: PDF 
摘要： 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.
关键词： Adaptive online prediction;  Least squares support vector regression (LS-SVR);  Electronic system    
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.
Key words: Adaptive online prediction    Least squares support vector regression (LS-SVR)    Electronic system
收稿日期: 2012-05-24 出版日期: 2012-12-09
CLC:  TP391  
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Yang-ming Guo
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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. Front. Inform. Technol. Electron. Eng., 2012, 13(12): 881-890.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1200156    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I12/881

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