单频干涉仪瞬时相位计算的神经网络模型

用反三角函数表述的单频干涉仪瞬时相位的解析计算模型通常是分段或不连续的 ,不利于系统性能的综合分析。这里提出了基于神经网络的干涉仪瞬时相位的连续型计算模型 ,给出了网络学习方法。仿真研究结果表明 ,该模型对干涉仪瞬时相位的辨识精度优于 0 5° ,同时对干涉仪的光强波动有良好的鲁棒性 ;实验结果验证了这一点 ,为进一步提高单频干涉仪信号处理精度奠定了基础。此外 ,简要述及了该模型在其他测量领域 ,特别是速度 加速度测量领域的应用前景。

Investigation of Neural Network Modeling for Instantaneous Phase in Single-Frequency Interferometry

The conventional expression of instantaneous phase of single frequency interferometer is based on inverse trigonometric functions and consequently is not continuous, which is generally not suitable for analyzing or improving the performance of interferometer synthetically. The feasibility of the Neural Network (NN) approach is investigated by simulations and experiment. Both simulating and experimental results show that the novel continuous model based on NN is applicable to practical, noisy signals, and the precision can be very good. Finally potential applications of the new NN approach, especially to the measurement of velocity and acceleration, are introduced.