摘要
外界温度变化与光纤应变都会导致光纤布拉格光栅(FBG)的中心波长发生偏移,由此引起FBG传感器在测量时的温度与应变交叉敏感问题。运用人工神经网络理论,建立FBG测量系统的BP神经网络模型,利用Matlab的神经网络工具箱,采取含动量项的梯度下降算法对网络进行训练,结果表明收敛速度较快。对训练后的网络进行验证,温度误差最大不超过2%,应变误差最大不超过5%,很好地实现了温度与应变的同时测量。
Outside temperature variation and fiber strain can raise center wavelengh excursion of fiber Bragg grating （FBG）, which will cause temperature and strain crossing susceptivity when measuring with FBG. A FBG system of BP neural network with artificial neural network theory is setup by using Matlab neural network toolbox, the network in gradient descent algorithm with momentum term is trained, and the results show convergence is faster. Temperature error of the network after training is less 2 %, and strain error is less 5 %, which prove the realization of simultaneous measurement of temperature and strain.
出处
《激光与光电子学进展》
CSCD
北大核心
2010年第6期118-121,共4页
Laser & Optoelectronics Progress
基金
河北省教育厅自然科学项目(Z2009460
Z2008459)资助课题
关键词
光纤布拉格光栅
人工神经网络
温度
应变
fiber Bragg grating
artificial neural network
temperature
strain
