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On-line Access: 2024-08-27

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Crosschecked: 2009-03-13

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.4 P.497-503

http://doi.org/10.1631/jzus.A0820282


Nonlinear multifunctional sensor signal reconstruction based on least squares support vector machines and total least squares algorithm


Author(s):  Xin LIU, Guo WEI, Jin-wei SUN, Dan LIU

Affiliation(s):  Department of Automatic Measurement and Control, Harbin Institute of Technology, Harbin 150001, China

Corresponding email(s):   xinliu@hit.edu.cn

Key Words:  Least squares support vector machine, Total least squares, Multifunctional sensor, Signal reconstruction


Xin LIU, Guo WEI, Jin-wei SUN, Dan LIU. Nonlinear multifunctional sensor signal reconstruction based on least squares support vector machines and total least squares algorithm[J]. Journal of Zhejiang University Science A, 2009, 10(4): 497-503.

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%T Nonlinear multifunctional sensor signal reconstruction based on least squares support vector machines and total least squares algorithm
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%A Jin-wei SUN
%A Dan LIU
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820282

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T1 - Nonlinear multifunctional sensor signal reconstruction based on least squares support vector machines and total least squares algorithm
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A1 - Guo WEI
A1 - Jin-wei SUN
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VL - 10
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SP - 497
EP - 503
%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0820282


Abstract: 
least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to multifunctional sensor signal reconstruction. For three different nonlinearities of a multifunctional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.031 84% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multifunctional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method.

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

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