提出了一种基于径向基函数(RBF)神经网络的光纤法布里-珀罗传感器解调方法, 从理论上分析了该方法的解调原理。从干涉谱中提取特征值, 利用干涉谱的特征值和腔长作为训练集, 对RBF网络进行训练, 训练好的网络就可以实现预测腔长的功能。在测量范围为0～2 MPa的法布里-珀罗(F-P)腔MEMS压力传感器进行的解调实验中, 该算法可以辨别0.1 MPa的压力, 腔长与压力数据的拟合度为0.98858。仿真计算得出, 该方法解调出的腔长的相对误差达到0.02% , 腔长的最大绝对误差小于0.1 μm。实验结果表明, 神经网络方法可以达到较高的精度, 满足实际需求。

Radial basis function network method is presented for demodulation of Fabry-Pérot pressure sensors, and its principle and error are analyzed theoretically. At first eigenvalue is extracted from interference spectrum, and with the eigenvalue of the spectrum and the length of the cavity the radial basis function network is trained. The trained network can forecast cavity length. In the experiment of demodulating MEMS Fabry-Pérot pressure sensor with metrical range from 0 to 2 MPa, its resolution reaches 0.1 MPa, and the linearity between the length of the cavity and pressure achieves 0.98858. In the simulation, the relative error of this new method is just 0.02% and the maximum absolute error of the length of the sensor cavity is less than 0.1 μm. The experiments show that this new method meets the practical demand with its high resolution.