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.
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Project supported by the National Natural Science Foundation of China (Nos. 60772007 and 60672008), and China Postdoctoral Science Foundation (No. 20070410258)
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Liu, X., Wei, G., Sun, Jw. et al. Nonlinear multifunctional sensor signal reconstruction based on least squares support vector machines and total least squares algorithm. J. Zhejiang Univ. Sci. A 10, 497–503 (2009). https://doi.org/10.1631/jzus.A0820282
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DOI: https://doi.org/10.1631/jzus.A0820282