Frontiers of Optoelectronics

, Volume 10, Issue 1, pp 62–69 | Cite as

Hybrid algorithm combining genetic algorithm with back propagation neural network for extracting the characteristics of multi-peak Brillouin scattering spectrum

  • Yanjun Zhang
  • Jinrui Xu
  • Xinghu Fu
  • Jinjun Liu
  • Yongsheng Tian
Research Article
  • 43 Downloads

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 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61675176), the Natural Science Foundation of Hebei Province (No. F2014203125), the Science and Technology Support Program of Hebei Province (Nos. 15273304D and 14273301D), and the “XinRuiGongCheng” Talent Project of Yanshan University.

References

  1. 1.
    Zhang Y, Li J, Meng C, Chen X, Dong W, Zhang X, Ruan S, Chen W. Hybrid optimization algorithm of Brillouin scattering spectra fitting. High Power Laser and Particle Beams, 2015, 27(9): 091013–1–091013-7Google Scholar
  2. 2.
    Zhang Y, Xu J, Fu X. Method of Brillouin scattering spectrum character extraction based on genetic algorithm and quantumbehaved particle swarm optimization hybrid algorithm. Chinese Journal of Lasers. 2016, 43(2): 0205002-1–0205002-10CrossRefGoogle Scholar
  3. 3.
    Liang H, Zhang X, Li X, Lu Y. Design and implementation of data fitting algorithm for Brillouin back scattered-light spectrum data. Acta Photonica Sinica, 2009, 38(4): 875–879Google Scholar
  4. 4.
    Liu X, Bao X. Brillouin spectrum in LEAF and simultaneous temperature and strain measurement. Journal of Lightwave Technology, 2012, 30(8): 1053–1059CrossRefGoogle Scholar
  5. 5.
    Zhao L, Xu Z, Li Y. An accurate and rapid method for extracting parameters from multi-peak Brillouin scattering spectra. Sensors and Actuators A, Physical, 2015, 232: 276–284CrossRefGoogle Scholar
  6. 6.
    Yin Z, Wu C, Gong W, Gong Z, Wang Y. Voigt profile function and its maximum. Acta Physica Sinica, 2013, 62(12): 123301–1–123301-5Google Scholar
  7. 7.
    Niklès M, Thévenaz L, Robert P A. Brillouin gain spectrum characterization in single-mode optical fibers. Journal of Lightwave Technology, 1997, 15(10): 1842–1851CrossRefGoogle Scholar
  8. 8.
    Zhang Z, Zhang P, Han S. Strain characteristic extraction of Brillouin spectrum based on general regression neural network. Chinese Journal of Lasers, 2013, 40(s1): s105008-1–s105008-6Google Scholar
  9. 9.
    Ida T, Ando M, Toraya H. Extended Pseudo-Voigt function for approximating the Voigt profile. Journal of Applied Crystallography, 2000, 33(6): 1311–1316CrossRefGoogle Scholar
  10. 10.
    Xie Z, Li X, Li C, Feng C. Forward kinematics of 3-PPR parallel mechanism based on displacement compensation of BP neural network. Computer Integrated Manufacturing Systems, 2015, 21(7): 1804–1809Google Scholar
  11. 11.
    Wang S, Wang X, Chen D, Wei M, Wang Z. Application of GA-BP neural network in detection of trace phosphate. Chinese Journal of Lasers, 2015, 42(5): 0515001-1–0515001-6CrossRefGoogle Scholar
  12. 12.
    Zhang J, Wan W, Zheng Z, Gan X, Zhu X. Research on X band extended cosecant squared beam synthesis of micro-strip antenna arrays using genetic algorithm. Acta Physica Sinica, 2015, 64(11): 110504–1–110504-9Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Yanjun Zhang
    • 1
  • Jinrui Xu
    • 1
  • Xinghu Fu
    • 1
  • Jinjun Liu
    • 2
  • Yongsheng Tian
    • 1
  1. 1.The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, School of Information Science and EngineeringYanshan UniversityQinhuangdaoChina
  2. 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 EngineeringYanshan UniversityQinhuangdaoChina

Personalised recommendations