Adaptive Neural Network Approach for Nonlinearity Compensation in Laser Interferometer

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• 作者：Gunhaeng Heo ; Wooram Lee ; Seungok Choi ; Jeehyong Lee ; Kwanho You
• 刊名：Lecture Notes in Computer Science
• 出版年：2007
• 出版时间：2007
• 年：2007
• 卷：4694
• 期：1
• 页码：251-258
• 全文大小：414 KB

文摘

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.