针对布里渊光时域反射光纤传感系统散射谱的高精度特征提取的要求, 提出了一种基于自适应变异果蝇优化算法和广义回归神经网络的布里渊散射谱特征提取算法。 不仅利用了广义回归神经网络在逼近能力、 学习速度、 模型的泛化等方面具有的优势, 而且采用搜索能力较强的自适应变异果蝇优化算法进一步增强了神经网络的学习能力, 从而提高了布里渊散射谱的拟合度和频移提取的准确度。 在布里渊散射谱中心频率为11.213 GHz, 线宽为40～50, 30～60和20～70 MHz的散射谱白噪声实验模型中, 将新算法分别与基于有限元分析的Levenberg-Marquardt拟合法、 粒子群优化和拉凡格式混合拟合法、 最小二乘法进行预测比较, 新算法获得的最大拟合频移误差为0.4 MHz, 平均拟合度为0.991 2, 均方根误差为0.024 1。 仿真结果表明所提出的算法拟合度较好, 绝对误差小。 因此, 将此算法用于基于布里渊光时域反射的分布式光纤传感系统, 可有效提高布里渊散射谱的拟合度和频移提取的准确度。

According to the high precision extracting characteristics of scattering spectrum in Brillouin optical time domain reflection optical fiber sensing system, this paper proposes a new algorithm based on flies optimization algorithm with adaptive mutation and generalized regression neural network. The method takes advantages of the generalized regression neural network which has the ability of the approximation ability, learning speed and generalization of the model. Moreover, by using the strong search ability of flies optimization algorithm with adaptive mutation, it can enhance the learning ability of the neural network. Thus the fitting degree of Brillouin scattering spectrum and the extraction accuracy of frequency shift is improved. Model of actual Brillouin spectrum are constructed by Gaussian white noise on theoretical spectrum, whose center frequency is 11.213 GHz and the linewidths are 40～50, 30～60 and 20～70 MHz, respectively. Comparing the algorithm with the Levenberg-Marquardt fitting method based on finite element analysis, hybrid algorithm particle swarm optimization, Levenberg-Marquardt and the least square method, the maximum frequency shift error of the new algorithm is 0.4 MHz, the fitting degree is 0.991 2 and the root mean square error is 0.024 1. The simulation results show that the proposed algorithm has good fitting degree and minimum absolute error. Therefore, the algorithm can be used on distributed optical fiber sensing system based on Brillouin optical time domain reflection, which can improve the fitting of Brillouin scattering spectrum and the precision of frequency shift extraction effectively.