|Table of Contents|

Research on the Fitting Algorithm of Multi-peakBrillouin Scattering Spectrum(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年01期
Page:
90-
Research Field:
·人工智能算法与应用专栏·
Publishing date:

Info

Title:
Research on the Fitting Algorithm of Multi-peakBrillouin Scattering Spectrum
Author(s):
Gao Yuan1Gao Jianjun1Du Jiahao1Kong Weibin123Wang Rugang13Zhou Feng13
(1.School of Information Technology,Yancheng Institute of Technology,Yancheng 224051,China)(2.State Key Laboratory of Millimeter Waves,Southeast University,Nanjing 210096,China)(3.Yancheng Optical Fiber Sensing and Application Engineering Technology Res
Keywords:
multi-peak Brillouin scattering spectrumRBF neural networkK-means clustering methodcurve fitting
PACS:
TN248.1
DOI:
10.3969/j.issn.1001-4616.2019.01.014
Abstract:
In order to solve the problem that simultaneous detection of temperature and strain in optical fiber sensing technology cannot be accurately obtained by using fiber multi-peak Brillouin scattering spectrum,the RBF neural network based on K-means clustering method is applied to the data fitting of optical fiber multi-peak Brillouin scattering spectrum. Firstly,the theoretical knowledge of Radial Basis Function(RBF)Neural Network and K-means clustering method is described. Secondly,it is concluded that different diffusion speeds will affect the fitting accuracy of the data by using RBF neural network algorithm to fit the data. However,the smoothness and accuracy of fitting curves cannot be guaranteed simultaneously. The RBF neural network based on K-means clustering method is used to fit the data and get more accurate fitting curve,and the mean square error is small.

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Last Update: 2019-03-30