Open AccessBack propagation neutral network based signal acquisition for Brillouin distributed optical fiber sensors
Author(s) -
Zeyuan Cao,
Nan Guo,
Meihong Li,
Kuanglu Yu,
Kaiqiang Gao
Publication year - 2019
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.004549
Subject(s) - sobel operator , artificial neural network , computer science , optics , brillouin zone , brillouin scattering , transfer function , backpropagation , optical fiber , fiber optic sensor , algorithm , edge detection , artificial intelligence , physics , image processing , electrical engineering , engineering , image (mathematics)
This manuscript proposes 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%.