概括
这项研究介绍了一种人工神经网络相位移算法 (ANNPA),以提高相位移干扰度的准确性. ANNPA有效地减少了相位转移误差,即使在非理想条件下也可以进行精确的测量.
科学领域:
- 光学计量学 在光学计量学
- 干涉测量是干涉测量的方法.
- 人工智能的人工智能
背景情况:
- 阶段移动误差限制了波长调节阶段移动干扰测量的准确性.
- 开发强大的算法对于精确的相位测量至关重要.
研究的目的:
- 提出和验证人工神经网络相位移算法 (ANNPA) 进行增强的相位移干扰测量.
- 为了抑制高阶相位移错误,并提高对线性错误和噪声的不敏感性.
主要方法:
- 设计和训练了一种人工神经网络相位移算法 (ANNPA).
- 优化了网络设计和计算步骤.
- 创建了一个专门的训练数据集,以抑制高阶相位移错误.
主要成果:
- 模拟证实了ANNPA对线性相位移错误和随机噪声的不敏感性.
- 使用Fizeau干扰仪的实验验证证明了ANNPA的有效性.
- 在非理想的条件下,ANNPA成功地进行了多表面相位移交干扰测量.
结论:
- 在干涉测量中,ANNPA提供了一种强大的解决方案,以减轻相位移错误.
- 该算法可以在具有挑战性的,非理想的实验设置中进行准确的相位测量.
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