激光与光电子学进展, 2020, 57 (19): 193002, 网络出版: 2020-09-23   

基于激光诱导击穿光谱与径向基函数神经网络的铝合金定量分析 下载: 800次

Quantitative Analysis of Aluminum Alloy Based on Laser-Induced Breakdown Spectroscopy and Radial Basis Function Neural Network
作者单位
南京航空航天大学机电学院, 江苏 南京 210001
引用该论文

潘立剑, 陈蔚芳, 崔榕芳, 李苗苗. 基于激光诱导击穿光谱与径向基函数神经网络的铝合金定量分析[J]. 激光与光电子学进展, 2020, 57(19): 193002.

Lijian Pan, Weifang Chen, Rongfang Cui, Miaomiao Li. Quantitative Analysis of Aluminum Alloy Based on Laser-Induced Breakdown Spectroscopy and Radial Basis Function Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(19): 193002.

参考文献

[1] 高安江, 王刚, 曲信磊, 等. 废铝再生预处理过程中的杂质分离和分类分选技术研究[J]. 再生资源与循环经济, 2015, 8(2): 33-36.

    Gao A J, Wang G, Qu X L, et al. The research on impurities separating and sorting technology in the pretreatment process of the aluminum scrap recycling[J]. Recyclable Resources and Circular Economy, 2015, 8(2): 33-36.

[2] 邱海鸥, 郑洪涛, 汤志勇. 岩石矿物分析[J]. 分析试验室, 2014, 33(11): 1349-1364.

    Qiu H O, Zheng H T, Tang Z Y. Analysis of rocks and minerals[J]. Chinese Journal of Analysis Laboratory, 2014, 33(11): 1349-1364.

[3] 陈娜, 刘尧香, 杜盛喆, 等. 纳秒、飞秒激光诱导击穿光谱技术的应用研究进展[J]. 激光与光电子学进展, 2016, 53(5): 050003.

    Chen N, Liu Y X, Du S Z, et al. Research progress in applications of nanosecond and femtosecond laser-induced breakdown spectroscopy[J]. Laser & Optoelectronics Progress, 2016, 53(5): 050003.

[4] 冯为蕾, 王福娟, 曾万祺, 等. 应用于LIBS的CCD光谱测量系统[J]. 激光与光电子学进展, 2013, 50(1): 013002.

    Feng W L, Wang F J, Zeng W Q, et al. CCD spectrum measurement system for laser induced breakdown spectroscopy[J]. Laser & Optoelectronics Progress, 2013, 50(1): 013002.

[5] 胡杨, 李子涵, 吕涛. 激光诱导击穿光谱结合人工神经网络测定地质标样中的铁含量[J]. 激光与光电子学进展, 2017, 54(5): 053003.

    Hu Y, Li Z H, Lü T. Quantitative measurement of iron content in geological standard samples by laser-induced breakdown spectroscopy combined with artificial neural network[J]. Laser & Optoelectronics Progress, 2017, 54(5): 053003.

[6] 林永增, 姚明印, 陈添兵, 等. 激光诱导击穿光谱检测赣南脐橙种植土壤的Cu和Cr[J]. 激光与光电子学进展, 2013, 50(5): 053002.

    Lin Y Z, Yao M Y, Chen T B, et al. Detection of Cu and Cr in the soil of navel orange plantation in Gannan by LIBS[J]. Laser & Optoelectronics Progress, 2013, 50(5): 053002.

[7] 李俊香, 杨友良, 孟凡伟, 等. 用于LIBS钢液在线定量分析的基体校正方法[J]. 激光与光电子学进展, 2013, 50(3): 031406.

    Li J X, Yang Y L, Meng F W, et al. Matrix correction method used for liquid steel online quantitative analysis by LIBS[J]. Laser & Optoelectronics Progress, 2013, 50(3): 031406.

[8] Kotzagianni M, Kakkava E, Couris S. Laser-induced breakdown spectroscopy (LIBS) for the measurement of spatial structures and fuel distribution in flames[J]. Applied Spectroscopy, 2016, 70(4): 627-634.

[9] Yaroshchyk P, Death D L, Spencer S J. Comparison of principal components regression, partial least squares regression, multi-block partial least squares regression, and serial partial least squares regression algorithms for the analysis of Fe in iron ore using LIBS[J]. Journal of Analytical Atomic Spectrometry, 2012, 27(1): 92-98.

[10] 余洋, 赵南京, 王寅, 等. 激光诱导击穿光谱单变量及多元线性回归方法研究[J]. 激光与光电子学进展, 2015, 52(9): 093001.

    Yu Y, Zhao N J, Wang Y, et al. Research on univariate and multiple linear regression calibration methods by laser induced breakdown spectroscopy[J]. Laser & Optoelectronics Progress, 2015, 52(9): 093001.

[11] 李越胜, 卢伟业, 赵静波, 等. 基于BP神经网络和激光诱导击穿光谱的燃煤热值快速测量方法研究[J]. 光谱学与光谱分析, 2017, 37(8): 2575-2579.

    Li Y S, Lu W Y, Zhao J B, et al. Detection of caloric value of coal using laser-induced breakdown spectroscopy combined with BP neural networks[J]. Spectroscopy and Spectral Analysis, 2017, 37(8): 2575-2579.

[12] 孙兰香, 于海斌, 丛智博, 等. 激光诱导击穿光谱技术结合神经网络定量分析钢中的Mn和Si[J]. 光学学报, 2010, 30(9): 2757-2765.

    Sun L X, Yu H B, Cong Z B, et al. Quantitative analysis of Mn and Si of steels by laser-induced breakdown spectroscopy combined with neural networks[J]. Acta Optica Sinica, 2010, 30(9): 2757-2765.

[13] 谷艳红, 赵南京, 马明俊, 等. 基于主成分回归的土壤重金属LIBS定量分析方法研究[J]. 光电子·激光, 2016, 27(7): 748-753.

    Gu Y H, Zhao N J, Ma M J, et al. Quantitative analysis of Cr in soils using LIBS with principal components regression[J]. Journal of Optoelectronics·Laser, 2016, 27(7): 748-753.

[14] 韩小孩, 张耀辉, 孙福军, 等. 基于主成分分析的指标权重确定方法[J]. 四川兵工学报, 2012, 33(10): 124-126.

    Han X H, Zhang Y H, Sun F J, et al. Method for determining index weight based on principal component analysis[J]. Journal of Sichuan Ordnance, 2012, 33(10): 124-126.

[15] 乔俊飞, 韩红桂. RBF神经网络的结构动态优化设计[J]. 自动化学报, 2010, 36(6): 865-872.

    Qiao J F, Han H G. Optimal structure design for RBFNN structure[J]. Acta Automatica Sinica, 2010, 36(6): 865-872.

潘立剑, 陈蔚芳, 崔榕芳, 李苗苗. 基于激光诱导击穿光谱与径向基函数神经网络的铝合金定量分析[J]. 激光与光电子学进展, 2020, 57(19): 193002. Lijian Pan, Weifang Chen, Rongfang Cui, Miaomiao Li. Quantitative Analysis of Aluminum Alloy Based on Laser-Induced Breakdown Spectroscopy and Radial Basis Function Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(19): 193002.

本文已被 2 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!