Wavelength Calibration Based on Back Propagation Neural Network

被引：0
|
作者：
Zhang, Liang [1 ]
Dai, Yinzhen [1 ]
Lin, Chun [1 ]
Lyu, Ruiqi [1 ]
Wang, Lei [1 ]
Hu, Tianlin [1 ]
机构：
[1] Xiamen Univ, Sch Phys & Mech & Elect Engn, Xiamen 361005, Peoples R China
来源：
PROCEEDINGS OF 2014 IEEE INTERNATIONAL CONFERENCE ON ANTI-COUNTERFEITING, SECURITY AND IDENTIFICATION (ASID) | 2014年
关键词：
wavelength calibration; neural network; grating spectrometer;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
A method based on Back Propagation Neural Network (BPNN) for wavelength calibration of Spectrometer is proposed in this paper. An appropriate neural network is constructed to map pixel location in CCD to the wavelength with any given grating position. In this method, input vector of the network involves the center wavelength of the spectrometer and the pixel location, output vector of the network is the spectral wavelength that has been corrected. And standard spectral lines from a Neon light are used for training of the neural network. Compared with traditional method based on correction formula, this method reduces the mean error by 55.6% with the center wavelength range from 608nm to 668nm.
引用
收藏
页码：109 / 112
页数：4
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