Share This Article:

Application of the Spectrum Peak Positioning Technology Based on BP Neural Network in Demodulation of Cavity Length of EFPI Fiber Optical Sensor

Abstract Full-Text HTML Download Download as PDF (Size:290KB) PP. 67-71
DOI: 10.4236/jcc.2013.17016    2,827 Downloads   4,437 Views   Citations

ABSTRACT

An Extrinsic Fabry-Perot Interferometric (EFPI) fiber optical sensor system is an online testing system for the gas density. The system achieves the measurement of gas density information mainly by demodulating the cavity length of EF- PI fiber optical sensor. There are many ways to achieve the demodulation of the cavity length. For shortcomings of the big intensity demodulation error and complex structure of phase demodulation, this paper proposes that BP neural net-work is used to locate the special peak points in normalized interference spectrum and combining the advantages of the unimodal and bimodal measurement achieves the demodulation of the cavity length. Through online simulation and actual measurement, the results show that the peak positioning technology based on BP neural network can not only achieve high-precision demodulation of the cavity length, but also achieve an absolute measurement of cavity length in large dynamic range.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Zhou, M. and Nie, M. (2013) Application of the Spectrum Peak Positioning Technology Based on BP Neural Network in Demodulation of Cavity Length of EFPI Fiber Optical Sensor. Journal of Computer and Communications, 1, 67-71. doi: 10.4236/jcc.2013.17016.

References

[1] Y. Jiang and W. H. Ding, “Recent Developments in Fiber Optic Spectral White-Light Interferometry,” Microwave and Optical Technology Letters, Vol. 1, No. 1, 2011, pp. 62-71.
[2] S.H. Kim, J.J. Lee, D.C. Lee and I.B. Kwon, “A Study on the Development of Transmission-Type Extrinsic Fabry-Perot Interferometric Optical Fiber Senor,” Journal of Lightwave Technology, Vol. 17, No.10, 1999, p. 1869.
[3] Y.J. Rao, “Recent Progress in Fiber-Optic Extrinsic Fabry-Perot Interferometric Sensors,” Optical Fiber Technology, Vol. 12, No. 3, 2006, pp. 227-237. http://dx.doi.org/10.1016/j.yofte.2006.03.004
[4] M. R. Zhou and Y. Zhang, “Application of Artificial Neural Network BP Algorithm in Near Infrared Spectroscopy,” Infrared, Vol. 11, 2006, pp. 1-4.
[5] M. R. Zhou, L. Y. Qi and L. Wang, “Applied Research of Neural Network BP Algorithm in Gas Absorption,” Spectrum Safety in Coal Mines, Vol. 10, 2008, pp. 1-3.
[6] X. Q. Cao, J. G. Zhu and R. Y. Tang, “Soft-Sensing Technology Based on Improved BP-Neural-Network,” Chinese Journal of Scientific Instrument, Vol. S1, 2005, pp. 185-186.
[7] Z. G. Jing, “Study on White Light Extrinsic Fabry-Perot Interferometric Fiber optical Sensor and Its Applications,” PhD Thesis, Dalian University of Technology, 2006.
[8] G. G. Amiri, A. Shahjouei, S. Saadat and M. Ajallooeian, “Hybrid Evolutionary-Neural Network Approach in Generation of Artificial Accelerograms Using Principal Component Analysis and Wavelet-Packet Transform,” Journal of Earthquake Engineering, Vol. 15, No. 1, 2011, pp. 50-76. http://dx.doi.org/10.1080/13632469.2010.517281
[9] N. Yadaiah and M. VeeraChary, “Adaptive Controller for Peak Power Tracking of Photovoltaic Systems,” Systems Analysis Modelling Simulation, Vol. 42, No. 9, 2010, pp. 1319-1344.
[10] S. C. Liu, Z. W. Yin, L. Zhang, X. F. Chen, L. Gao and J. C. Cheng, “Dual-Wavelength FBG Laser Sensor Based on Photonic Generation of Radio Frequency Demodulation Technique,” Journal of Electromagnetic Waves and Applications, Vol. 23, No. 16, 2012, pp. 2177-2185. http://dx.doi.org/10.1163/156939309790109252

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.