摘要
借助深度学习算法的非线性处理能力,提出基于注意力机制和长短期记忆(LSTM)网络的温漂预测模型,从而对法布里-珀罗(F-P)滤波器进行温漂补偿。针对温漂数据中复杂的时空信息,采用LSTM提取时间信息,利用注意力机制分配空间权重。实验结果表明:在升温-降温-升温环境下,所提方法和LSTM模型的最大波长漂移误差分别为6.75 pm和16.64 pm;在单调降温环境下,两种方法的最大波长漂移误差分别为5.39 pm和14.09 pm。所提方法在最大绝对误差(MAXE)、均方根误差(RMSE)和平均误差(MAE)上均优于最小二乘支持向量机(LSSVM)和循环神经网络(RNN)。
Objective The fiber Fabry-Perot(F-P)filter plays a critical role in fiber Bragg grating(FBG)wavelength demodulation systems.However,the continuous drift in the transmission wavelength and driving voltage curve of the F-P filter due to changes in the ambient temperature can significantly decrease the wavelength demodulation accuracy.To correct the drift error,researchers have proposed several wavelength correction methods,such as the F-P etalon,FBG reference grating,and gas absorption line reference methods.Despite high accuracy,these methods can increase the system's cost and complexity.Recently,with the increased applications of artificial intelligence,machine learning methods have emerged as a novel and highly portable option for correcting temperature drift errors in F-P filter at a relatively low cost.Currently,the most commonly employed technique for temperature drift correction is support vector machine(SVM),which does not take into account the high temporal correlation among samples before and after temperature drift data.To this end,we propose an Attention-LSTM network-based temperature drift correction method for F-P filters.The temperature drift data for the F-P filter is a typical time series with dynamic characteristics,indicating that the current drift depends both on the present input and the past input.We adopt the LSTM model for feature extraction and apply the attention mechanism to assign different weights to various input features.The combination of short-term and long-term memory,along with the attention mechanism,enhances the demodulation accuracy of the F-P filter.Methods We select FBG0 as the reference grating and the other three FBGs as sensing gratings.The input features employed in the model include temperature,temperature change rate,and the spectral position of FBG0.The output of the model is the absolute wavelength drift of sensing FBG3.Due to the strong temporal correlation in the temperature drift data of the F-P filter,a fixed length of time series samples is first selected,and the
作者
盛文娟
胡俊
彭刚定
Sheng Wenjuan;Hu Jun;Peng Gangding(College of Automation Engineering,Shanghai University of Electric Power,Shanghai 200090,China;College of Electrical Engineering and Telecommunications,University of New South Wales,Sydney 2052,New South Wales,Australia)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2023年第22期57-64,共8页
Acta Optica Sinica
基金
国家自然科学基金(61905139)
国家自然科学基金重点项目(61935002)。
关键词
光栅
光纤光栅
法布里-珀罗滤波器
温漂误差
注意力机制
长短期记忆网络
gratings
fiber Bragg gratings
Fabry-Perot filters
temperature drift error
attention mechanism
long shortterm memory network
