This paper introduces a method to do simultaneous temperature estimations of multiple gratings in an FBG array. The method involves training a multi-layer neural network using simulated training data. The network is then used to estimate the temperature changes of multiple gratings simultaneously using only the experimentally obtained spectrum of the FBG array. The versatility of the method is seen from the results of three different setups. That is the broadband lightsource with OSA, the broadband lightsource with spectrometer, and incoherent optical frequency domain reflectometry. The method can estimate the temperature changes with high accuracy and low root mean squared error (RMSE) for the setups under consideration. Finally, the method is shown to be capable of simultaneous estimation of temperature changes for 19 FBGs using the BLS setup with a maximum absolute error of $6.64~\mathrm {\textbf {K}}$ and an RMSE of $1.69~\mathrm {\textbf {K}}$ in only 1.74 ms/FBG.

中文翻译：

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使用多层神经网络同时估计多个光栅的温度

本文介绍了一种对 FBG 阵列中的多个光栅进行同时温度估计的方法。该方法涉及使用模拟训练数据训练多层神经网络。然后使用网络仅使用实验获得的 FBG 阵列光谱同时估计多个光栅的温度变化。从三种不同设置的结果中可以看出该方法的多功能性。即带OSA的宽带光源、带光谱仪的宽带光源、非相干光频域反射计。该方法可以为所考虑的设置以高精度和低均方根误差 (RMSE) 估计温度变化。最后， $6.64~\mathrm {\textbf {K}}$ 和一个 RMSE $1.69~\mathrm {\textbf {K}}$ 仅 1.74 ms/FBG。