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

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...

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Bibliographic Details
Published in:Journal of Zhejiang University. A. Science 2009-04, Vol.10 (4), p.497-503
Main Authors: Liu, Xin, Wei, Guo, Sun, Jin-wei, Liu, Dan
Format: Article
Language:English
Subjects:
Civil Engineering
Classical and Continuum Physics
Engineering
Industrial Chemistry/Chemical Engineering
Mechanical Engineering
传感器模型
信号重构
多功能传感器
总体最小二乘
最小二乘支持向量机
最小二乘算法
训练算法
非线性
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Summary: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 0.00136%, 0.03184% 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.
ISSN:1673-565X
1862-1775
DOI:10.1631/jzus.A0820282