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[A Brillouin Scattering Spectrum Feature Extraction Based on Flies Optimization Algorithm with Adaptive Mutation and

Yan-jun Zhang, Wen-zhe Liu, Xing-hu Fu

    Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
    |February 25, 2016
    PubMed
    Summary

    A novel algorithm combining a generalized regression neural network and a flies optimization algorithm improves Brillouin scattering spectrum fitting and frequency shift extraction accuracy in optical fiber sensing systems.

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    Area of Science:

    • Optical Fiber Sensing
    • Signal Processing
    • Machine Learning

    Context:

    • Brillouin optical time domain reflection (BOTDR) systems are crucial for distributed optical fiber sensing.
    • Accurate extraction of Brillouin scattering spectrum characteristics is essential for precise measurements.
    • Existing methods face challenges in achieving high fitting degrees and accuracy.

    Purpose:

    • To propose a new algorithm for enhanced Brillouin scattering spectrum analysis.
    • To leverage the strengths of generalized regression neural networks (GRNN) and a flies optimization algorithm (FOA) with adaptive mutation.
    • To improve the accuracy of frequency shift extraction in BOTDR systems.

    Summary:

    • A novel algorithm integrates GRNN for its approximation and learning capabilities with FOA for robust search.
    • The algorithm was tested on simulated Brillouin spectra with added Gaussian white noise.
    • Performance was compared against established methods like Levenberg-Marquardt and particle swarm optimization.

    Impact:

    • The proposed algorithm demonstrates a high fitting degree (0.9912) and minimal frequency shift error (0.4 MHz).
    • It significantly enhances the fitting of Brillouin scattering spectra and the precision of frequency shift extraction.
    • The algorithm offers a promising solution for improving the performance of distributed optical fiber sensing systems.