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A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy
Zhang P(张鹏); Sun LX(孙兰香); Kong HY(孔海洋); Yu HB(于海斌); Guo MT(郭美亭); Zeng P(曾鹏)
作者部门工业控制网络与系统研究室
会议名称Applied Optics and Photonics China: Optical Spectroscopy and Imaging, AOPC 2017
会议日期June 4-6, 2017
会议地点Beijing, China
会议录名称AOPC 2017: Optical Spectroscopy and Imaging
出版者SPIE
出版地Bellingham, WA
2017
页码1-10
收录类别EI ; CPCI(ISTP)
EI收录号20180404675566
WOS记录号WOS:000425516200006
产权排序1
ISSN号0277-786X
ISBN号978-1-5106-1403-1
关键词Laser Induced Breakdown Spectroscopy Genetic Algorithm Principal Component Analysis Artificial Neural Networks Spectral Segment Selection Classification
摘要Selection of characteristic lines is a critical work for both qualitative and quantitative analysis of laser-induced breakdown spectroscopy; it usually needs a lot of time and effort. A novel method combining genetic algorithm, principal component analysis and artificial neural networks (GA-PCA-ANN) is proposed to automatically extract the characteristic spectral segments from the original spectra, with ample feature information and less interference. On the basis of this method, three selection manners: selecting the whole spectral range, optimizing a fixed-length segment and optimizing several non-fixed-length sub-segments were analyzed; and their classification results of steel samples were compared. It is proved that selecting a fixed-length segment with an appropriate segment length achieves better results than selecting the whole spectral range; and selecting several non-fixed-length sub-segments obtains the best result with smallest amount of data. The proposed GA-PCA-ANN method can reduce the workload of analysis, the usage of bandwidth and cost of spectrometers. As a result, it can enhance the classification capability of laser-induced breakdown spectroscopy.;  
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/21538
专题工业控制网络与系统研究室
通讯作者Sun LX(孙兰香)
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Key Laboratory of Networked Control System, CAS, Shenyang 110016, China
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Zhang P,Sun LX,Kong HY,et al. A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy[C]. Bellingham, WA:SPIE,2017:1-10.
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