- About EITEE
- Articles Online
- For Authors
- For Reviewers
- Features
- Subscription & Service
- Personal Center
- Logout
FOLLOWUS
- About EITEE
- Introduction
- Editorial Board
- Corresponding Experts
- Events
- Honors
- Contact Us
- Articles Online
- Current Issue
- Archive
- Special Issues
- Virtual Issues
- Highlights
- In Press
- For Authors
- Submit
- Call for papers
- Guide for Authors
- Ethics & Disclosures
- Fee & Funding
- Downloads
- For Reviewers
- Guide for Reviewers
- Ethics & Disclosures
- Thanks card
- Features
- FITEE Forum
- Engineering Front
- Paper Videos
- Editors’ Corner
- Other Resources
- Subscription & Service
- Subscription
- Services
Adaptive online prediction method based on LS-SVR and its application in an electronic system
- Adaptive online prediction method based on LS-SVR and its application in an electronic system
- Frontiers of Information Technology & Electronic Engineering Vol. 13, Issue 12, Pages: 881-890(2012)
- Affiliations：
School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Author bio：
[ "Yang-ming GUO, E-mail: yangming_g@nwpu.edu.cn" ]
- Funds：
DOI：10.1631/jzus.C1200156
CLC： TP391Received：24 May 2012，
Revised：2012-11-12，
Published：2012-12
- Accepted：
Scan QR Code
Yang-ming GUO, Cong-bao RAN, Xiao-lei LI, et al. Adaptive online prediction method based on LS-SVR and its application in an electronic system[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(12): 881-890.
Yang-ming GUO, Cong-bao RAN, Xiao-lei LI, et al. Adaptive online prediction method based on LS-SVR and its application in an electronic system[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(12): 881-890. DOI： 10.1631/jzus.C1200156.
摘要
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.
关键词
Keywords
references
G.C. Cawley , , , N.L.C. Talbot . . Reduced rank kernel ridge regression . . Neur. Process. Lett. , , 2002 . . 16 ( ( 3 ): ): 293 - - 302 . . DOI: 10.1023/A:1021798002258 http://doi.org/10.1023/A:1021798002258 . .
A.J. Chen , , , Z.H. Song , , , P. Li . . Modeling method of least squares support vector regression based on vector base learning . . Control Theory Appl. , , 2007 . . 24 ( ( 1 ): ): 1 - - 5 . . . .
S.L. Chen , , , G. Chen , , , H. Xue . . Research on sparsification approach for least squares support vector machine classification . . Comput. Eng. , , 2011 . . 37 ( ( 22 ): ): 145 - - 147, 150 . . . .
C.C. Cowen . . Linear Algebra for Engineering and Science . . Indiana West Pickle Press, West Lafayette, USA , , 1996 . . p.124 - - 265 . . . .
B.J. de Kruif , , , T.J.A. de Vries . . Pruning error minimization in least squares support vector machines . . IEEE Trans. Neur. Networks , , 2003 . . 14 ( ( 3 ): ): 696 - - 702 . . DOI: 10.1109/TNN.2003.810597 http://doi.org/10.1109/TNN.2003.810597 . .
J.L. Deng . . The Primary Methods of Grey System Theory . . Huazhong University of Science and Technology Press, Wuhan, China , , 2004 . . p.34 - - 45 . . . .
H.B. Guo , , , X.Q. Guan . . Application of Least Squares Support Vector Regression in Network Flow Forecasting . . 2nd Int. Conf. on Computer Engineering and Technology , , 2010 . . p.V7-342 - - V7-345 . . DOI: 10.1109/ICCET.2010.5485452 http://doi.org/10.1109/ICCET.2010.5485452 . .
Y.M. Guo , , , Z.J. Zhai , , , H.M. Jiang . . Weighted prediction of multi-parameter chaotic time series using least squares support vector regression (LS-SVR) . . J. Northwestern Polytechn. Univ. , , 2009 . . 27 ( ( 1 ): ): 83 - - 86 . . . .
L. Hoegaerts , , , J.A.K. Suykens , , , J. Vandewalle , , , B. de Moor . . A comparison of pruning algorithms for sparse least squares support vector machines . . LNCS , , 2004 . . 3316 1247 - - 1253 . . DOI: 10.1007/978-3-540-30499-9_194 http://doi.org/10.1007/978-3-540-30499-9_194 . .
L.C. Jiao , , , L.F. Bo , , , L. Wang . . Fast sparse approximation for sparse least squares support vector machine . . IEEE Trans. Neur. Networks , , 2007 . . 18 ( ( 3 ): ): 685 - - 697 . . DOI: 10.1109/TNN.2006.889500 http://doi.org/10.1109/TNN.2006.889500 . .
S.S. Keerthi , , , S.K. Shevade . . SMO algorithm for least squares SVM formulations . . Neur. Comput. , , 2003 . . 15 ( ( 2 ): ): 487 - - 507 . . DOI: 10.1162/089976603762553013 http://doi.org/10.1162/089976603762553013 . .
K.R. Müller , , , A.J. Smola , , , G. Rätsch , , , B. Schölkopf , , , J. Kohlmorgen , , , V.N. Vapnik . . Predicting time series with support vector machines . . LNCS , , 1997 . . 1327 999 - - 1004 . . DOI: 10.1007/BFb0020283 http://doi.org/10.1007/BFb0020283 . .
H.X. Pang , , , W.D. Dong , , , Z.H. Xu , , , H.J. Feng , , , Q. Li , , , Y.T. Chen . . Novel linear search for support vector machine parameter selection . . J. Zhejiang Univ.-Sci. C (Comput. & Electron.) , , 2011 . . 12 ( ( 11 ): ): 885 - - 896 . . DOI: 10.1631/jzus.C1100006 http://doi.org/10.1631/jzus.C1100006 . .
J. Qu , , , M.J. Zuo . . An LSSVR-based algorithm for online system condition prognostics . . Expert Syst. Appl. , , 2012 . . 39 ( ( 5 ): ): 6089 - - 6102 . . DOI: 10.1016/j.eswa.2011.12.002 http://doi.org/10.1016/j.eswa.2011.12.002 . .
B. Schölkoph , , , J. Burges , , , A. Smola . . Advance in Kernel Methods: Support Vector Machines , , : : Cambridge, Massachusetts, USA MIT Press , , 1999 . . p.55 - - 90 . . . .
J.A.K. Suykens , , , J. Vandewale . . Least squares support vector machine classifiers . . Neur. Process. Lett. , , 1999 . . 9 ( ( 3 ): ): 293 - - 300 . . DOI: 10.1023/A:1018628609742 http://doi.org/10.1023/A:1018628609742 . .
J.A.K. Suykens , , , L. Lukas , , , J. Vandewalle . . Sparse Approximation Using Least Squares Support Vector Machines . . Proc. IEEE Int. Symp. on Circuits and Systems , , 2000 . . p.757 - - 760 . . DOI: 10.1109/ISCAS.2000.856439 http://doi.org/10.1109/ISCAS.2000.856439 . .
J.A.K. Suykens , , , T.V. Gestel , , , J.D. Brabanter , , , B.D. Moor , , , J. Vandewaller . . Least Squares Support Vector Machines . . World Scientific Publishing, New Jersey, USA , , 2002 . . p.71 - - 111 . . DOI: 10.1142/9789812776655_0003 http://doi.org/10.1142/9789812776655_0003 . .
V.N. Vapnik . . The Nature of Statistical Learning Theory , , : : New York, USA Springer-Verlag , , 1995 . . p.267 - - 287 . . . .
V.N. Vapnik . . Statistical Learning Theory , , : : John Wiley and Sons Hoboken, USA , , 1998 . . p.93 - - 110 . . . .
E. Yaakov , , , M. Shie , , , M. Ron . . Sparse online greedy support vector regression . . LNCS , , 2002 . . 2430 1 - - 3 . . DOI: 10.1007/3-540-36755-1_8 http://doi.org/10.1007/3-540-36755-1_8 . .
X.Y. Zeng , , , X.W. Chen . . SMO-based pruning methods for sparse least squares support vector machines . . IEEE Trans. Neur. Networks , , 2005 . . 16 ( ( 6 ): ): 1541 - - 1546 . . DOI: 10.1109/TNN.2005.852239 http://doi.org/10.1109/TNN.2005.852239 . .
H.R. Zhang , , , X.D. Wang . . Incremental and online learning algorithm for regression least squares support vector machine . . Chin. J. Comput. , , 2006 . . 29 ( ( 3 ): ): 400 - - 406 . . . .
Y.H. Zhao , , , P. Zhong , , , K.N. Wang . . Application of least squares support vector regression based on time series in prediction of gas . . J. Conv. Inf. Technol. , , 2011 . . 6 ( ( 1 ): ): 243 - - 250 . . DOI: 10.4156/jcit.vol6.issue1.28 http://doi.org/10.4156/jcit.vol6.issue1.28 . .
X.R. Zhou , , , Z.S. Teng , , , Z. Yi . . Fast pruning algorithm for designing sparse least squares support vector machine . . Electr. Mach. Control , , 2009 . . 13 ( ( 4 ): ): 626 - - 630 . . . .
- L-PDFDownload
- Meta-XMLDownload
- BibTeXDownload
- EndnoteDownload
- RefMan Download
- NoteExpressDownload
- NoteFirstDownload
Publicity Resources
Related Articles
Related Author
Related Institution
Chat- Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
- Tel：+86-571-88273162 Email：fitee@zju.edu.cn
- Copyright © 2000 - 2025, Zhejiang University Press 京ICP备09064830号-19
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
