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Frontiers of Optoelectronics

ISSN 2095-2759

ISSN 2095-2767(Online)

CN 10-1029/TN

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Front. Optoelectron.    2017, Vol. 10 Issue (1) : 62-69    https://doi.org/10.1007/s12200-017-0654-3
RESEARCH ARTICLE
Hybrid algorithm combining genetic algorithm with back propagation neural network for extracting the characteristics of multi-peak Brillouin scattering spectrum
Yanjun ZHANG1,Jinrui XU1,Xinghu FU1(),Jinjun LIU2,Yongsheng TIAN1
1. The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2. Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Key Laboratory of Advanced Forging & Stamping Technology and Science, College of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
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Abstract

In this study, a hybrid algorithm combining genetic algorithm (GA) with back propagation (BP) neural network (GA-BP) was proposed for extracting the characteristics of multi-peak Brillouin scattering spectrum. Simulations and experimental results show that the GA-BP hybrid algorithm can accurately identify the position and amount of peaks in multi-peak Brillouin scattering spectrum. Moreover, the proposed algorithm obtains a fitting degree of 0.9923 and a mean square error of 0.0094. Therefore, the GA-BP hybrid algorithm possesses a good fitting precision and is suitable for extracting the characteristics of multi-peak Brillouin scattering spectrum.
Keywords fiber optics      Brillouin scattering spectrum      genetic algorithm (GA)      back propagation (BP) neural network      multi-peak spectrum     
Corresponding Author(s): Xinghu FU   
Online First Date: 29 December 2016    Issue Date: 17 March 2017
 Cite this article:   
Yanjun ZHANG,Jinrui XU,Xinghu FU, et al. Hybrid algorithm combining genetic algorithm with back propagation neural network for extracting the characteristics of multi-peak Brillouin scattering spectrum[J]. Front. Optoelectron., 2017, 10(1): 62-69.
 URL:  
https://academic.hep.com.cn/foe/EN/10.1007/s12200-017-0654-3
https://academic.hep.com.cn/foe/EN/Y2017/V10/I1/62
Fig.1   BP neural network model
Fig.2  Flow diagram of the GA-BP hybrid algorithm
Fig.3  Comparison of fitting results among three different values of Rsn: (a) Rsn = 20 dB; (b) Rsn = 25 dB; and (c) Rsn = 30 dB
Rsn/dB R2 MSE Brillouin frequency shift error/MHz
peak1 peak2 peak3
20 0.8240 0.0912 1.4 1.5 1.3
25 0.9275 0.0540 1.0 1.1 1.0
30 0.9899 0.0186 0.6 0.5 0.8
Tab.1  Fitting results among three different values of Rsn
Fig.4  Fitting results of multi-peak Brillouin scattering spectrum in different conditions: (a) DvB1 = DvB2 = 40 MHz; (b) DvB1 = DvB2 = 60 MHz; (c) DvB1 = DvB2 = 80 MHz; and (d) DvB1 = DvB2 = 100 MHz
Rsn/dB linewidth/MHz R2 MSE
20 40 0.8681 0.0913
60 0.8345 0.0741
80 0.9576 0.0724
100 0.8515 0.0740
25 40 0.9814 0.0297
60 0.9550 0.0337
80 0.9314 0.0297
100 0.9047 0.0240
30 40 0.9690 0.0380
60 0.9552 0.0328
80 0.9121 0.0341
100 0.9083 0.0294
Tab.2  Fitting results of different parameters
Fig.5  Comparison of fitting results among four algorithms: (a) PSO; (b) GA-QPSO; (c) BP; and (d) GA-BP
algorithm R2 MSE running time/s
PSO 0.4261 0.3076 2.32
GA-QPSO 0.4096 0.4377 10.20
BP 0.8704 0.0664 3.27
GA-BP 0.9899 0.0186 11.53
Tab.3  Comparison of MSE, R2, and running time among four algorithms in simulation results
Fig.6  Principle diagram of the experimental system. DFB-LD: distributed feedback-laser diode, AOM: acousto-optic modulator, EDFA:?erbium-doped fiber amplifier, PC: polarization controller, EOM: electro-optic modulator, DC: direct current, PS: polarization scrambler, DBPD: double balance photoelectric detector, DAQ: digital acquisition
Fig.7  Comparison of experimental results among four algorithms: (a) PSO; (b) GA-QPSO; (c) BP; and (d) GA-BP
algorithm R2 MSE running time/s
PSO 0.6401 0.1663 2.02
GA-QPSO 0.3809 0.1937 10.09
BP 0.9836 0.0137 3.91
GA-BP 0.9923 0.0094 10.76
Tab.4  Comparison of MSE, R2, and running time among four algorithms in experimental results
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