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Application of LS-SVM by GA for Reducing Cross-Sensitivity of Sensors

In: Proceedings of 2012 3rd International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012)

Author

Listed:
  • Wen-bin Zhang

    (Kunming University of Science and Technology
    Postdoctoral Workstation of Yunnan Power Grid Corporation)

  • Jing-ling Chen

    (Kunming University of Science and Technology)

  • Chun-guang Suo

    (Kunming University of Science and Technology)

  • Wen-sheng Gui

    (Kunming University of Science and Technology)

Abstract

Least square support vector machine (LS-SVM) is widely used in the regression analysis, but the prediction accuracy greatly depends on the parameters selection. In this paper, Simple Genetic Algorithm is applied to optimize the LS-SVM parameters; correspondingly, the prediction accuracy is improved. Sensors are always sensitive to several parameters, and this phenomenon is called cross-sensitivity which restricts the application of sensors in engineering. In order to reduce cross-sensitivity, the model of multi-sensor system measurement is established in this paper. For solving the nonlinear problems in the model, LS-SVM is used to establish the inverse model. It proves that the method has a high forecasting precision. It is beneficial to the application of sensors.

Suggested Citation

  • Wen-bin Zhang & Jing-ling Chen & Chun-guang Suo & Wen-sheng Gui, 2013. "Application of LS-SVM by GA for Reducing Cross-Sensitivity of Sensors," Springer Books, in: Runliang Dou (ed.), Proceedings of 2012 3rd International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012), edition 127, chapter 0, pages 711-720, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-33012-4_71
    DOI: 10.1007/978-3-642-33012-4_71
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