Identification of Invoice Based on Laser-Induced Photoluminescence Spectrum
YANG Qin1,YANG Yong2,TIAN Yong-hong1
1. School of Physical Science and Technology, Yangtze University, Jingzhou 434023, China 2. School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
Abstract:The rapid identification of invoice authenticity was studied based on laser-induced photoluminescence spectrum. First, the spectral curves of eighty invoice samples were obtained by laser-induced photoluminescence detection system, and genetic algorithm (GA) was applied to fit and separate overlapped spectral region between 566 and 669 nm by three Gaussian peaks. Spectral feature parameters extracted by GA were employed as the inputs of BP neural networks, and then an identification model was built. One hundred and four data were converted to 13 Gaussian parameters, and for authentic and false invoices the coefficients of determination (R2) were 0.997 89 and 0.996 83 and the relative standard deviations (RSD) were 0.017 052 and 0.022 362, respectively. It was showed that Gaussian fitting algorithm could not only simplify the parameters of models, but also improve the explanation of analysis models. Through comparison analysis of the results, it was found that the model, whose thirteen feature parameters and two evaluated parameters were all applied as BP inputs, was the best, and the corrected identification rates of sixty calibration samples and twenty validation samples were both 100%. So the identification method studied in the present research played a good role in the classification and identification, and offered a new approach to the rapid identification of invoice authenticity.
[1] ZHAO Qi-zhu(赵启柱). Journal of Huainan Institute of Technology(淮南工业学院学报),2002,22(2):30. [2] CHEN Shu-gen,JIANG He-ping,ZHOU Gao-bei(陈树根,江和平,周高杯). Infrared and Laser Engineering(红外与激光工程),2001,30(4):174. [3] Ohta H,Portugall O,Ubrig N,et al. Journal of Low Temperature Physics,2010,159(1-2):203. [4] Yan Bing,Lin Lixia,Wu Jianhua,et al. Journal of Fluorescence,2011,21(1):203. [5] Holland J H. Adaptation in Natural and Artificial Systems. Ann Arbor,MI:University of Michigan Press,Ann Arbor,MI,1975. [6] Tayi N,Ggü M T,zaka M. Computational Optimization and Applications,2008,41(3):377. [7] Sabri Kocer,M Rahmi Canal. Journal of Medical Systems,2009. Available Online. http://springer.lib.tsinghua.edu.cn/content/j362366625471648/fulltext.pdf.