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The Application of Support Vector Regression in the Dual-Axis Tilt Sensor Modeling

  • Conference paper
Life System Modeling and Intelligent Computing (ICSEE 2010, LSMS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 6330))

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Abstract

This paper investigates the dual-axis tilt sensor modeling using support vector regression (SVR). To implement a dual-axis tilt measurement system, the designing structure of this system is firstly presented. Then, to overcome the nonlinear between the input and output signals, support vector regression (SVR) is used to model the input and output of the tilt sensor. Finally, a real dual-axis tilt measurement system experimental platform is constructed, which can provide a lot of experimental data for SVR modeling. Experiments of different modeling ways for the dual-axis tilt sensor are compared. Experimental results show that the proposed modeling scheme can effectively improve the modeling precision.

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Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Power Station Automation Technology, School of Mechanical Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Wei Su & Jingqi Fu

Authors
  1. Wei Su
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  2. Jingqi Fu
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Editor information

Editors and Affiliations

  1. Electrical Engineering and Computer Science, The Queen’s University Belfast, Ashby Building, Stranmillis Road, BT9 5AH, Belfast, UK

    Kang Li

  2. School of Mechatronical Engineering and Automation, Shanghai University, P.O.Box 183, 200072, Shanghai, China

    Li Jia , Xin Sun  & Minrui Fei ,  & 

  3. School of Electronics, Electrical Engineering and Computer Science, The Queen’s University Belfast, Ashby Building, Stranmillis Road, BT9 5AH, Belfast, UK

    George W. Irwin

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© 2010 Springer-Verlag Berlin Heidelberg

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Su, W., Fu, J. (2010). The Application of Support Vector Regression in the Dual-Axis Tilt Sensor Modeling. In: Li, K., Jia, L., Sun, X., Fei, M., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science(), vol 6330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15615-1_58

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  • DOI: https://doi.org/10.1007/978-3-642-15615-1_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15614-4

  • Online ISBN: 978-3-642-15615-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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