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Optimized neural network for temperature extraction from Brillouin scattering spectra

机译：从布里渊散射光谱温度提取优化神经网络

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摘要

The nonlinear mapping characteristic of artificial neural network (ANN) is suitable for temperature extraction from Brillouin scattering spectra in optical fiber sensing system. To further improve the generalization ability of neural network, an optimized method for ANN training is proposed in this paper. Firstly, a set of noisy training data with different linewidth under various temperatures and frequency scanning intervals is constructed by using Pseudo-Voigt function and is entered into networks for training. Then, the ANNs with optimized parameters are tested by the measured Brillouin scattering spectra, which are from an established Brillouin optical time domain reflectometry (BOTDR) sensing system. Finally, the temperature distribution information of the sensing fiber is extracted directly. The experimental results show that the ANNs trained by the proposed method obtain better temperature extraction accuracy than that obtained by other ANNs, which indicates that the generalization ability and adaptability of ANN are enhanced for temperature extraction in Brillouin optical fiber sensing system.
机译：人工神经网络（ANN）的非线性映射特性适用于光纤传感系统中布里渊散射光谱的温度提取。为了进一步提高神经网络的泛化能力，本文提出了一种优化的ANN培训方法。首先，通过使用伪voigt函数构建在各种温度和频率扫描间隔下具有不同线宽的一组噪声训练数据，并输入培训的网络。然后，通过测量的布里渊散射光谱测试具有优化参数的ANN，其来自建立的布里渊光学时域反射测量仪（BOTDR）感测系统。最后，直接提取感测光纤的温度分布信息。实验结果表明，所提出的方法训练的ANN获得比其他ANN所获得的更好的温度提取精度，这表明在布里渊光纤传感系统中提高了ANN的泛化能力和适应性。

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