[1]康维新,李慧,韩月.基于DE-GRNN算法的布里渊散射谱拟合[J].应用科技,2019,46(03):46-50.[doi:10.11991/yykj.201809003]
 KANG Weixin,LI Hui,HAN Yue.A fitting method based on DE-GRNN for Brillouin scattering spectrum[J].Applied science and technology,2019,46(03):46-50.[doi:10.11991/yykj.201809003]
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基于DE-GRNN算法的布里渊散射谱拟合(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第46卷
期数:
2019年03期
页码:
46-50
栏目:
现代电子技术
出版日期:
2019-04-29

文章信息/Info

Title:
A fitting method based on DE-GRNN for Brillouin scattering spectrum
作者:
康维新 李慧 韩月
哈尔滨工程大学 信息与通信工程学院, 黑龙江 哈尔滨 150001
Author(s):
KANG Weixin LI Hui HAN Yue
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
光纤光学布里渊散射谱差分进化广义回归神经网络曲线拟合故障检测特征提取拟合精度
Keywords:
fiber opticsBrillouin scattering spectrumdifferential evolution (DE)general regression neural network (GRNN)curve fittingfault detectionfeature exctractionfitting precision
分类号:
TN247
DOI:
10.11991/yykj.201809003
文献标志码:
A
摘要:
为了提高布里渊光时域分析(BOTDA)型分布式光纤传感技术的布里渊散射谱特征提取精度,提出一种基于差分进化算法(DE)优化广义回归神经网络(GRNN)的曲线拟合算法,通过利用差分进化算法实现对广义回归神经网络的光滑因子自动寻优,减少人为测试的繁杂性。仿真实验结果显示,该混合优化算法在不同信噪比及线宽的条件下,对布里渊散射谱具有较好的拟合度,最佳拟合度可达0.99以上,最小均方根误差为0.012 0,拟合性能优于传统布里渊散射谱拟合算法。
Abstract:
In order to improve the feature extraction accuracy of Brillouin scattering spectrum based on Brillouin optical time domain analysis (BOTDA) distributed optical fiber sensor technology, a curve fitting algorithm based on differential evolution algorithm (DE) is proposed to optimize general regression neural network (GRNN). The DE algorithm is used to automatically optimize the smoothing factor of GRNN and reduce the complexity of human testing. Simulation results show that the hybrid optimization algorithm has good fitting performance for Brillouin scattering spectrum under different signal-noise-ratio and linewidth. The optimum fitting degree can be above 0.99, the minimum root mean square error is 0.0120, and the fitting performance is better than the traditional Brillouin scattering algorithms.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-09-04。
作者简介:康维新,男,教授,博士;李慧,女,硕士研究生
通讯作者:李慧,E-mail:li_hui@hrbeu.edu.cn
更新日期/Last Update: 2019-04-29