Optimized neural network for temperature extraction from Brillouin scattering spectra

The nonlinear mapping characteristic of artificial neural network (ANN) is suitable for temperature extraction from Brillouin scattering spectra in optical fiber sensing system. To further improve the generalization ability of neural network, an optimized method for ANN training is proposed in this paper. Firstly, a set of noisy training data with different linewidth under various temperatures and frequency scanning intervals is constructed by using Pseudo-Voigt function and is entered into networks for training. Then, the ANNs with optimized parameters are tested by the measured Brillouin scattering spectra, which are from an established Brillouin optical time domain reflectometry (BOTDR) sensing system. Finally, the temperature distribution information of the sensing fiber is extracted directly. The experimental results show that the ANNs trained by the proposed method obtain better temperature extraction accuracy than that obtained by other ANNs, which indicates that the generalization ability and adaptability of ANN are enhanced for temperature extraction in Brillouin optical fiber sensing system.