A Multi-Peak Brillouin Scattering Spectrum Feature Extraction Method Based on Multi-Criteria Decision-Making and Particle Swarm Optimization-Levenberg Marquardt Hybrid Optimization Algorithm
ZHANG Yan-jun1,2, JIA Wei1, FU Xing-hu1,2*, LI Da1, YU Chun-juan1
1. School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China 2. The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China
Abstract：As to fitting the multi-peaks Brillouin scattering spectrum with traditional method, the maximum power point is usually selected as the benchmark while other extreme value points which are less than the maximum power are lost. The fitting curve only has one peak because the multi-peaks Brillouin scattering spectrum is simplified into the highest peak and several small peaks. So it will lead to the loss of useful information. In order to improve the feature extraction accuracy of Brillouin scattering spectrum, a hybrid optimization algorithm named MCDM-PSO-LM algorithm is presented based on MCDM and PSO-LM algorithm. The MCDM algorithm can identify and locate the peaks and valleys of multi-peaks Brillouin scattering spectrum accurately. The PSO-LM hybrid algorithm can realize the curve fitting on every peak and valley, and it can seach the center frequency shift of each peak. The PSO-LM hybrid algorithm can solves these disadvantages, which PSO algorithm premature convergence to local minimum and LM algorithm depends on the initial value problem. It can also combine the global search ability of PSO algorithm and the local search ability of LM algorithm. Compared with traditional algorithms, MCDM-PSO-LM algorithm can ensure the solving speed and accuracy to the optimal value, and the analytical solution will be close to the optimal value sufficiently. So it improves the operation ability. With different signal to noise ratio and linewidth, the results of frequency shift and temperature error show that the MCDM-PSO-LM method can locate every peak and valley of multi-peaks Brillouin scattering spectrum accurately. Thus, it can be used for the feature extraction of multi-peaks Brillouin scattering spectrum. The recognition effect of this method is obviously better than that of traditional algorithms and it can improve the accuracy of information analysis.
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