期刊
OPTIK
卷 172, 期 -, 页码 753-759出版社
ELSEVIER GMBH, URBAN & FISCHER VERLAG
DOI: 10.1016/j.ijleo.2018.07.064
关键词
Fiber optic sensors; Fiber Bragg gratings; Temperature metrology; Fitting; BP neural network
类别
资金
- Tianjin Higher School Science and Technology Development Fund Project [20140404]
A novel method which applies BP neural network (BPNN) to temperature calibration of fiber Bragg grating (FBG) sensors is proposed and discussed. Processing and analysis of experimental data showed that this method fitted very well the complex relationship between the center wavelength of FBG and temperature which is approximately linear in room temperature whereas nonlinear in low temperature. The maximum absolute error and root mean squared error were respectively 0.9434 degrees C, 0.2102 degrees C in fitting and 0.8943 degrees C, 0.2081 degrees C in testing which verified the advantage of BPNN fitting compared with the previous polynomial fitting. The forecasting performance of BPNN was also satisfactory. The novel FBG temperature calibration method based on BPNN has considerable application prospect in FBG temperature measurement.
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