一种光纤测温信号的非线性补偿方法

Nonlinear compensation method for optical fiber temperature measurement signal

针对分布式光纤测温系统中光电子器件温度蠕变和光纤传输损耗导致信号衰减的问题,提出了一种基于径向基(RBF)神经网络的分布式光纤测温系统信号的非线性补偿方法。首先,通过温度-距离-输出信号三者之间的非线性关系,建立RBF神经网络补偿模型;其次,对数据进行预处理,选取大量有效的数据对模型进行重复训练,提高模型的精确性;最后,利用反馈控制原理控制误差自动调整散布常数,加速网络收敛。实验结果表明:该方法能够有效补偿非线性信号损耗,提高分布式光纤测温系统的准确性,具有一定的实用性和有效性。

Aiming at the signal attenuation caused by temperature creep and fiber transmission loss of optoelectronic devices in distributed optical fiber temperature measurement system,a nonlinear signal compensation method for distributed optical fiber temperature measurement system based on radial basis function(RBF)neural network is proposed.Firstly,a RBF neural network compensation model is established by the nonlinear relationship between temperature-distance-output signals.Then,the data is preprocessed,and a large amount of valid data is selected to repeatedly train the model to improve the accuracy of the model.Finally,using the feedback control principle,the control error automatically adjusts the scatter constant to accelerate network convergence.The experimental results show that the model can effectively compensate the nonlinear signal loss and improve the accuracy of the distributed optical fiber temperature measurement system,which has certain practicability and effectiveness.