基于布里渊效应的分布式光纤传感器以其可在沿光纤中同时获得被测量场时间和空间上的连续分布信息，成为当前国际的研究热点。根据光纤中布里渊散射谱的传输特点和高精度特征提取的要求，提出了利用莱文伯马夸特(L-M)算法调节权值的径向基函数神经网络(RBFN)对布里渊散射谱进行特征提取。通过与反向传播(BP)神经网络、五次多项式曲线拟合法和三次样条插值法进行预测比较，在中心频率为11.213 GHz，权重比为4∶1的仿真散射谱模型中，本方法相对误差最小，仅0.0015179%，温度相对误差仅为0.152 ℃，且拟合度较好。在不同脉宽和不同温度下的同一检测系统中，前者的综合评价指标优于其他三种拟合方法。数值分析和实验研究均表明径向基函数神经网络适用于对布里渊散射谱进行拟合，有效提高了预测精度。

Distributed optical fiber sensing system based on Brilouin scattering has attracted wide attention for its ability of sensing the measured field by detecting the continuously distributed information in time and space. Considering the trait of the spectral shape variance during the Brillouin scattering process in optical fiber and the requirement of high accuracy, a novel method based on radial basis function neural (RBFN) networks in which the output layer weights are adjusted by Levenberg-Marquardt method is presented. A model of actual Brillouin spectrum is constructed by Gaussian white noise on the theoretical spectrum, the core frequency is 11.213 GHz and the weight is 4∶1. Comparing the proposed algorithm with traditional back propagation (BP) neural networks, polynomial five times curve fitting and piecewise cubic spline interpolation, the relative error of the new method is 0.0015179% and the temperature error is 0.152 ℃. The appraised parameters are better than other three algorithms at the same test system under different pulse widths and temperatures. The numerical and experimental results show that the RBFN networks is suitable for the fitting of Brillouin scattering spectrum, and the forecast accuracy is improved efficiently.