基于DE-GRNN算法的布里渊散射谱拟合

A fitting method based on DE-GRNN for Brillouin scattering spectrum

为了提高布里渊光时域分析(BOTDA)型分布式光纤传感技术的布里渊散射谱特征提取精度,提出一种基于差分进化算法(DE)优化广义回归神经网络(GRNN)的曲线拟合算法,通过利用差分进化算法实现对广义回归神经网络的光滑因子自动寻优,减少人为测试的繁杂性。仿真实验结果显示,该混合优化算法在不同信噪比及线宽的条件下,对布里渊散射谱具有较好的拟合度,最佳拟合度可达0.99以上,最小均方根误差为0.012 0,拟合性能优于传统布里渊散射谱拟合算法。

In order to improve the feature extraction accuracy of Brillouin scattering spectrum based on Brillouin optical time domain analysis(BOTDA)distributed optical fiber sensor technology,a curve fitting algorithm based on differential evolution algorithm(DE)is proposed to optimize general regression neural network(GRNN).The DE algorithm is used to automatically optimize the smoothing factor of GRNN and reduce the complexity of human testing.Simulation results show that the hybrid optimization algorithm has good fitting performance for Brillouin scattering spectrum under different signal-noise-ratio and linewidth.The optimum fitting degree can be above 0.99,the minimum root mean square error is 0.0120,and the fitting performance is better than the traditional Brillouin scattering algorithms.