Back propagation neural network based signal acquisition for Brillouin distributed optical fiber sensors
We proposed a method based on back propagation (BP) neural network and the spectral subtraction method to quickly obtain sensing information in Brillouin fiber optics sensors. BP neural network’s characteristics which can realize any complex nonlinear mapping help to determine the frequency shift section(s) information. The training function, transfer function and number of hidden layer nodes of BP neural network are determined with experimental data. The experimental results show that comparing with traditional Lorentz fitting algorithm and edge detection with Sobel operator, the BP neural network is about 1/12 in terms of time complexity with the Lorentz algorithm, about 1/9 with the edge detection based on Sobel operator; while the respective accuracy on determine the frequency shifted section(s) has improved by 79.4% and 27.9%.
We have provided this Optics Express paper [Ref] for better understanding.
Ref: Zhiyuan Cao, Nan Guo, Meihong Li, Kuanglu YU, et al, Back propagation neural network based signal acquisition for Brillouin distributed optical fiber sensors, Optics Express, 2019/02, 27(4), 4549-4561.
First Online Date: 10:00 Beijing Time, May. 11th, 2022