| Information Science |
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| Nonlinear multifunctional sensor signal reconstruction based on least squares support vector machines and total least squares algorithm |
| Xin LIU, Guo WEI, Jin-wei SUN, Dan LIU |
| Department of Automatic Measurement and Control, Harbin Institute of Technology, Harbin 150001, China |
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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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Received: 14 April 2008
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