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题名:
A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy
作者: 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
通讯作者: Sun LX(孙兰香)
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: AOPC 2017: Optical Spectroscopy and Imaging
会议录出版者: SPIE
会议录出版地: Bellingham, WA
出版日期: 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.
英文摘要:  
语种: 英语
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内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/21538
Appears in Collections:工业控制网络与系统研究室_会议论文

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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

Recommended Citation:
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]. Applied Optics and Photonics China: Optical Spectroscopy and Imaging, AOPC 2017. Beijing, China. June 4-6, 2017.A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy.
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