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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)
Department工业控制网络与系统研究室
Conference NameApplied Optics and Photonics China: Optical Spectroscopy and Imaging, AOPC 2017
Conference DateJune 4-6, 2017
Conference PlaceBeijing, China
Source PublicationAOPC 2017: Optical Spectroscopy and Imaging
PublisherSPIE
Publication PlaceBellingham, WA
2017
Pages1-10
Indexed ByEI ; CPCI(ISTP)
EI Accession number20180404675566
WOS IDWOS:000425516200006
Contribution Rank1
ISSN0277-786X
ISBN978-1-5106-1403-1
KeywordLaser Induced Breakdown Spectroscopy Genetic Algorithm Principal Component Analysis Artificial Neural Networks Spectral Segment Selection Classification
AbstractSelection 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.;  
Language英语
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Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/21538
Collection工业控制网络与系统研究室
Corresponding AuthorSun LX(孙兰香)
Affiliation1.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
GB/T 7714
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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