Laser & Optoelectronics Progress, Volume. 57, Issue 5, 053002(2020)
Quantitative Analysis of Liquid Steel Element in LIBS Using SVR Improved by Particle Swarm Optimization
[7] Clegg S M, Sklute E, Dyar M D et al. Multivariate analysis of remote laser-induced breakdown spectroscopy spectra using partial least squares, principal component analysis, and related techniques[J]. Spectrochimica Acta Part B: Atomic Spectroscopy, 64, 79-88(2009).
[10] Xu H M, Wang H Y, Liang J et al. Support vector machine regression algorithm and its application[J]. Journal of Beijing Institute of Petro-Chemical Technology, 18, 62-66(2010).
[11] Chen G, Zhou J. Research on parameters and forecasting interval of support vector regression model to small sample[J]. Acta Metrologica Sinica, 29, 92-96(2008).
[12] Guo S X, Wang Y F, Chen A. Prediction on huge database on the regression model of support vector machine[J]. Computer Engineering and Applications, 43, 12-14, 32(2007).
[14] Yi W Z. Hybrid algorithm of chaotic catfish particle swarm optimization and differential evolution[J]. Computer Engineering and Applications, 48, 54-58, 87(2012).
[18] Bekefi G. Radiation processes in plasmas[M]. New York: Wiley(1966).
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Youliang Yang, Lu Wang, Cuihong Ma. Quantitative Analysis of Liquid Steel Element in LIBS Using SVR Improved by Particle Swarm Optimization[J]. Laser & Optoelectronics Progress, 2020, 57(5): 053002
Category: Spectroscopy
Received: Jul. 8, 2019
Accepted: Aug. 28, 2019
Published Online: Mar. 5, 2020
The Author Email: Lu Wang (1099209804@qq.com)