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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。
The analytical model of the instantaneous phase of a single frequency interferometer, which is expressed by an inverse trigonometric function, is usually fragmented or discontinuous, which is not conducive to the comprehensive analysis of system performance. This paper presents a continuous model of the instantaneous phase of the interferometer based on neural network, and gives the method of network learning. Simulation results show that the identification accuracy of this model is better than 0 5 ° for the instantaneous phase of the interferometer and good robustness to the light intensity fluctuation of the interferometer. The experimental results verify this point. In order to further improve the single-frequency interferometer Signal processing accuracy laid the foundation. In addition, the application prospect of this model in other measurement fields, especially in the field of velocity acceleration measurement is briefly described.