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Title: Back propagation neutral network based signal acquisition for Brillouin distributed optical fiber sensors
Authors: Cao, ZY
Guo, N 
Li, MH
Yu, KL
Gao, KQ
Issue Date: 2019
Publisher: Optical Society of America
Source: Optics express, 18 Feb. 2019, v. 27, no. 4, p. 4549-4561 How to cite?
Journal: Optics express 
Abstract: 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%.
EISSN: 1094-4087
DOI: 10.1364/OE.27.004549
Rights: © 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement (
The following publication Zhiyuan Cao, Nan Guo, Meihong Li, Kuanglu Yu, and Kaiqiang Gao, "Back propagation neutral network based signal acquisition for Brillouin distributed optical fiber sensors," Opt. Express 27, 4549-4561 (2019) is available at
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