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14 September 2019 Stable temperature calibration method of fiber Bragg grating based on radial basis function neural network
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Abstract

A stable temperature calibration method based on radial basis function neural network (RBFNN) is proposed to obtain the complex relationship between the temperature and the center wavelength of fiber Bragg grating (FBG) with high accuracy and excellent stability. We introduce the regularized and generalized RBFNN, respectively, and test the accuracy of trained models. Experimental results demonstrate that the maximum absolute error (MAE) and root-mean-squared error (RMSE) of regularized RBFNN are 1.1098°C and 0.1982°C, respectively, in fitting and 1.0206°C and 0.1997°C, respectively, in testing, and the MAE and RMSE of generalized RBFNN are 1.1099°C and 0.1982°C, respectively, in fitting and 1.0209°C and 0.1997°C, respectively, in testing. Compared with existing methods, such as polynomial fitting and back propagation neural network, RBFNN has significantly improved the accuracy and stability of FBG temperature calibration and has considerable application prospect in FBG temperature measurement.

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2019/$28.00 © 2019 SPIE
Yang An, Xiaocen Wang, Zhigang Qu, Tao Liao, Liqun Wu, and Zhongliang Nan "Stable temperature calibration method of fiber Bragg grating based on radial basis function neural network," Optical Engineering 58(9), 096105 (14 September 2019). https://doi.org/10.1117/1.OE.58.9.096105
Received: 6 February 2019; Accepted: 26 August 2019; Published: 14 September 2019
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