光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
Spectroscopy and Spectral Analysis
2015年
10期
2916-2923
,共8页
张燕君%刘文哲%付兴虎%毕卫红
張燕君%劉文哲%付興虎%畢衛紅
장연군%류문철%부흥호%필위홍
分布式光纤传感%布里渊散射谱%自适应变异果蝇算法%广义回归神经网络
分佈式光纖傳感%佈裏淵散射譜%自適應變異果蠅算法%廣義迴歸神經網絡
분포식광섬전감%포리연산사보%자괄응변이과승산법%엄의회귀신경망락
Distributed optical fiber sensing%Brillouin scattering spectrum%Flies optimization algorithm with adaptive mutation%Generalized regression neural network
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 M Hz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 M Hz ,平均拟合度为0.9912,均方根误差为0.0241。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。
針對佈裏淵光時域反射光纖傳感繫統散射譜的高精度特徵提取的要求,提齣瞭一種基于自適應變異果蠅優化算法和廣義迴歸神經網絡的佈裏淵散射譜特徵提取算法。不僅利用瞭廣義迴歸神經網絡在逼近能力、學習速度、模型的汎化等方麵具有的優勢,而且採用搜索能力較彊的自適應變異果蠅優化算法進一步增彊瞭神經網絡的學習能力,從而提高瞭佈裏淵散射譜的擬閤度和頻移提取的準確度。在佈裏淵散射譜中心頻率為11.213 GHz ,線寬為40~50,30~60和20~70 M Hz的散射譜白譟聲實驗模型中,將新算法分彆與基于有限元分析的Levenberg‐Marquardt擬閤法、粒子群優化和拉凡格式混閤擬閤法、最小二乘法進行預測比較,新算法穫得的最大擬閤頻移誤差為0.4 M Hz ,平均擬閤度為0.9912,均方根誤差為0.0241。倣真結果錶明所提齣的算法擬閤度較好,絕對誤差小。因此,將此算法用于基于佈裏淵光時域反射的分佈式光纖傳感繫統,可有效提高佈裏淵散射譜的擬閤度和頻移提取的準確度。
침대포리연광시역반사광섬전감계통산사보적고정도특정제취적요구,제출료일충기우자괄응변이과승우화산법화엄의회귀신경망락적포리연산사보특정제취산법。불부이용료엄의회귀신경망락재핍근능력、학습속도、모형적범화등방면구유적우세,이차채용수색능력교강적자괄응변이과승우화산법진일보증강료신경망락적학습능력,종이제고료포리연산사보적의합도화빈이제취적준학도。재포리연산사보중심빈솔위11.213 GHz ,선관위40~50,30~60화20~70 M Hz적산사보백조성실험모형중,장신산법분별여기우유한원분석적Levenberg‐Marquardt의합법、입자군우화화랍범격식혼합의합법、최소이승법진행예측비교,신산법획득적최대의합빈이오차위0.4 M Hz ,평균의합도위0.9912,균방근오차위0.0241。방진결과표명소제출적산법의합도교호,절대오차소。인차,장차산법용우기우포리연광시역반사적분포식광섬전감계통,가유효제고포리연산사보적의합도화빈이제취적준학도。
According to the high precision extracting characteristics of scattering spectrum in Brillouin optical time domain reflec‐tion optical fiber sensing system ,this paper proposes a new algorithm based on flies optimization algorithm with adaptive muta‐tion 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 Bril‐louin 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 M Hz ,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 M Hz ,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 ab‐solute error .Therefore ,the algorithm can be used on distributed optical fiber sensing system based on Brillouin optical time do‐main reflection ,which can improve the fitting of Brillouin scattering spectrum and the precision of frequency shift extraction ef‐fectively .