基于YSPSO-ＲBFN 的布里渊散射谱特征提取

为提高提取到的传感布里渊散射谱的布里渊频偏量的精度，利用压缩因子粒子群优化算法调节权值的ＲBFN( Ｒadial Basis Function Net) 径向基函数神经网络对布里渊散射谱进行特征提取。提出的算法克服了传统ＲBFN 神经网络易于陷入局部极值的缺点，利用PSO( Particle Swarm Optimization) 算法调节权值后向传输网络，对布里渊散射谱进行精度提取，保证了求解的速度和精度。数值分析过程中，利用新型结合算法在不同线宽和不同信噪比大测量范围情况下的散射谱进行参数估计。通过实验获得不同温度下的布里渊散射谱数据，利用YSPSO-ＲBFN( Particle Swarm Optimization with Shinkage Factor Shirnhage Factor-Ｒadical Basis Function) 算法处理实验数据，结果表明，该算法可提高布里渊散射谱特征提取的精度，25 ℃下拟合误差为1． 99 MHz，温度升高拟合误差也在减小。在85 ℃时的频移拟合误差为0． 047 MHz。因此，将该算法用于布里渊散射温度和应变传感系统，可有效提高检测精度。

Spectrum Feature Extraction Method Based on YSPSO-RBFN High-Precision Brillouin Scattering

In order to improve the extraction to the sensing of brillouin scattering spectrum of the accuracy of brillouin frequency deviation，the squeezing factor of using particle swarm optimization algorithm is used to adjust the weights of ＲBFN ( Ｒadial Basis Function Net) network． The proposed algorithm overcomes the shortcoming of traditional ＲBFN neural network which is easy to fall into local extremum． Using PSO ( Particle Swarm Optimization) algorithm after adjust the weights to the transmission network，precision of brillouin scattering spectrum are extracted，ensurimg the solution speed and precision． In the process of numerical analysis，a new algorithm is used to estimate the scattering spectra of different line width and different SNＲ( Signal Noise Ｒatio) at different temperatures． Through the experiment the brillouin scattering spectrum data are obtained． Using YSPSOＲBFN( Particle Swarm Optimization with Shinkage Factor Shirnhage Factor-Ｒadical Basis Function) algorithm to deal with the experimental data，the results show that the algorithm can improve the accuracy of brillouin scattering spectrum feature extraction，the fitting error is 1． 99 MHz，under 25 ℃ when temperature fitting error is reduced．In 85 ℃ the frequency shift of fitting error is 0． 047 MHz． Therefore，when the algorithm is applied to the scattering temperature and strain sensing system of brillouin，it has great application prospect in improving detection accuracy．