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Proceedings Paper

Identification chemical agent simulants by remote infrared spectra with improved artificial neural network
Author(s): Xiao-qiang Dong; Teng-xiao Guo; Yu-Ting Yan; Ji Wang; Xu Zhang; Jun-ming Li
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Paper Abstract

Remote infrared sensing is a good approach to detect Chemical agents which can prevent operator being poisoned. The pattern recognition algorithms such as artificial neural network are the core of the chemical agent spectra identification subsystem. This paper presents a modified artificial neural network that can effectively train and identify chemical agent remote sensing spectra. The C++ language was used to program the identification software. Then many remote sensing spectra DMMP as chemical agent simulants were used to train the artificial neural network. The results show that the adaptive momentum and adaptive learning rate accelerate the artificial neural network convergence, cross-examination avoids neural network over-fitting, and the modified artificial neural network can be used to identify chemical agents remote sensing spectra perfectly.

Paper Details

Date Published: 8 March 2017
PDF: 5 pages
Proc. SPIE 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 102553V (8 March 2017); doi: 10.1117/12.2268044
Show Author Affiliations
Xiao-qiang Dong, Tsinghua Univ. (China)
The State Key Lab. of NBC Protection for Civilian (China)
Teng-xiao Guo, The State Key Lab. of NBC Protection for Civilian (China)
Yu-Ting Yan, Tsinghua Univ. (China)
Ji Wang, Tsinghua Univ. (China)
Xu Zhang, The State Key Lab. of NBC Protection for Civilian (China)
Jun-ming Li, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 10255:
Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016
Yueguang Lv; Jialing Le; Hesheng Chen; Jianyu Wang; Jianda Shao, Editor(s)

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