为了简化布里渊散射提取温度的步骤并提高提取精度,提出利用径向基函数神经网络直接通过布里渊散射谱获取温度特征的一种新方案;将各温度布里渊散射谱作为训练集计算出温度模型,将待测布里渊散射谱直接输入至模型即可获取温度;对比平滑拟合、反向传播神经网络、径向基函数神经网络3种方案对温度测量的效果,分别选取扫频频率间隔为0.175,1,5,10,20 MHz时的77组数据,并对不同线宽进行扩展。结果表明:基于径向基函数神经网络方法的均方根误差较小,且随步进频率增加而增长缓慢;步进频率为20 MHz时,单线宽误差达到0.8002 ℃,多线宽误差为1.0814 ℃,分别是平滑拟合测量温度方法误差的33.04%和42.88%,是反向传播神经网络均方根误差的40.25%和55.89%;基于径向基函数神经网络的方法在一定程度上减少了计算步骤,提高了收敛性。

To simplify the temperature extraction steps for Brillouin scattering and also improve the extraction precision, we propose a new method for directly obtaining the temperature characteristics of Brillouin gain spectra based on the radial basis function neural network. The Brillouin scattering spectra at various temperatures are used as the training set to establish the temperature model. The temperature can be obtained through directly inputting the Brillouin spectra into the model. The effects of three methods of smooth fitting, back propagation neural network and radial basis function neural network on the temperature measurements are compared. In the experiment, 77 groups of data at sweeping frequency intervals of 0.175, 1, 5, 10, and 20 MHz are selected and also those at different linewidths are expanded. The results show that, the root-mean-square error (RMSE) based on the radial basis function neural network is relatively small. Moreover, the RMSE increases slowly with the increase of step frequency. When the step frequency is 20 MHz, the error of single line width is up to 0.8002 ℃ and that of multiple line width is 1.0814 ℃, 33.04% and 42.88% of that by the smooth fitting method, and 40.25% and 55.89% of that by the back propagation neural network, respectively. The convergence is improved to a certain extent as a result of calculation step reduction in the method based on the radial basis function neural network.