The application of support vector regression in the dual-axis tilt sensor modeling

Authors:
Wei Su;Jingqi Fu
Affiliations:
Shanghai Key Laboratory of Power Station Automation Technology, School of Mechanical Engineering and Automation, Shanghai University, Shanghai, China;Shanghai Key Laboratory of Power Station Automation Technology, School of Mechanical Engineering and Automation, Shanghai University, Shanghai, China
Venue:
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Year:
2010

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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.