Center Wavelength Detection Algorithm of FBG Based on Neural Network

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Abstract:

In order to find a more accurate center wavelength detection fitting algorithm for fiber Bragg grating (FBG) reflection spectrum, Based on the theory of neural network, we designed different neural network to study and fit the spectral data which has been acquired from FBG.In this paper,we have studied BP,RBFand wavelet neural network structure,comparing the network training time and the result of fitting curves ,wavelet neural network is proved to be the best fitting algorithm.

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1001-1004

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September 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Byoungho Lee. Review of the Present Status of Optical Fiber Sensors [J]. Optical fiber Technology, 2003(9): 57-79.

DOI: 10.1016/s1068-5200(02)00527-8

Google Scholar

[2] Fu Hui Xuan, Zhao Hong. application and design of neural network based on MATLAB[M] Beijing: Mechanical Industry Press, (2007).

Google Scholar

[3] Wang Qiang, Yu Yuefeng, Zhang Haojiong. Function approximation based on Artificial neural network[J]. Computer Simulation, (2002).

Google Scholar

[4] WU Li-feng, WU Jing-long. The Research of the adaptive learning rate about BP algorithm[J]. DA ZHONG KE JI. 2011, 12: 16-18.

Google Scholar

[5] YANG Yin-dong, ZHOU Yong-quan, LI Tao-shen, HOU Zhan-wei. Method for curve reconstruction based on radial basis function network[J]. Computer Engineering and Design. 2007, 28(10): 2405-2407.

Google Scholar

[6] Zhu Xiaoguang. HRTFs approximation and audio simulation based on wavelet neural network[J]. Journal of Computer Research and Development, 2000, 37 (6) : 703-709.

Google Scholar

[7] Deng Chao, Zhang Tao, Yao Qinghua. A method of impulse noise filtering method based on the wavelet neural network[J]. North University of China (Natural Science Edition), 2010, 31 (5) : 504-508.

Google Scholar