Abstract
In this paper, we propose a compensation algorithm to reduce the nonlinearity error which is occurred in a heterodyne laser interferometer as a nano-meter scale measurement apparatus. In heterodyne laser interferometer, frequency-mixing is the main factor of nonlinearity error. Using an RLS algorithm, the nonlinearity compensation parameters are found to be used through geometric projection. With the roughly modified intensity signals from LIA, the back-propagation neural network algorithm minimizes the objective function to track the reference signal for learning period. Through some experiments, it is verified that the proposed algorithm can reduce nonlinear factors and improve the measurement accuracy of laser interferometer.
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© 2007 Springer-Verlag Berlin Heidelberg
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Heo, G., Lee, W., Choi, S., Lee, J., You, K. (2007). Adaptive Neural Network Approach for Nonlinearity Compensation in Laser Interferometer. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74829-8_31
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DOI: https://doi.org/10.1007/978-3-540-74829-8_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74828-1
Online ISBN: 978-3-540-74829-8
eBook Packages: Computer ScienceComputer Science (R0)