Abstract
In the shield tunneling construction, the inclinometer is usually utilized to measure the pitching angle and rolling angle of the shield machine in real-time. However, the nonlinearity and the temperature characteristic of the inclinometer always result in large measurement error. In order to improve its measurement accuracy, the research on compensating the nonlinear error and the temperature drift error based on BP neural network is presented in this paper. The characteristic of the inclinometer is studied by experiment at first and then its inverse model is built using BP neural network and trained with an amount of experimental data; finally the trained model is used to compensate the measurement error. The experimental results verify that the proposed compensation method can improve the measurement accuracy of the inclinometer greatly by correcting the nonlinearity and eliminating the influence of temperature.
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© 2009 Springer-Verlag Berlin Heidelberg
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Wen, X., Cai, H., Pan, M., Zhu, G. (2009). Compensation of Measurement Error for Inclinometer Based on Neural Network. In: Xie, M., Xiong, Y., Xiong, C., Liu, H., Hu, Z. (eds) Intelligent Robotics and Applications. ICIRA 2009. Lecture Notes in Computer Science(), vol 5928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10817-4_46
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DOI: https://doi.org/10.1007/978-3-642-10817-4_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10816-7
Online ISBN: 978-3-642-10817-4
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