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核技术  2013 

基于FOA-LSSVM混合优化模型钛铁间基体效应的校正研究

DOI: DOI:10.11889/j.0253-3219.2013.hjs.36.120202, PP. 120202-120202

Keywords: 果蝇算法,基体效应,能量色散X射线荧光,最小二乘支持向量机

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Abstract:

提出用果蝇算法(FOA)优化最小二乘支持向量机(LSSVM)的混合模型校正能量色散X荧光(EDXRF)分析中铁和钛的基体效应。使用国产CIT-3000SMEDXRF分析仪、电制冷半导体探测器测得五类矿样共80组光谱数据,每类矿样16组光谱数据。运用FOA优化LSSVM的参数并建立最优模型预测30个样本的钛铁含量,对比化学分析值,FOA-LSSVM预测的钛、铁元素含量与化学分析值的相对误差在1%以内的共26个样本,占总量的86.67%;其余4个样本的钛铁含量预测值与化学分析值一致,占总量的13.33%。另外,运用粒子群算法(PSO)、遗传算法(GA)优化的LSSVM和MATLAB默认的LSSVM模型预测钛铁含量,将其与FOA-LSSVM模型预测的结果进行了对比。综合研究表明,FOA-LSSVM能够实现钛铁元素间基体效应的校正,是一种优选方法。

References

[1]  [ 1 ] 曹利国,丁益民,黄志琦.能量色散X荧光方法[M].成都:成都科技大学出版社,1998.182-274
[2]  CAO Liguo,DING Yimin,HUANG Zhiqi. Method of Energy Dispersive X-ray Fluorescence [M]. Chengdu:Chengdu University of Science and Technology Press,1998, 182-274
[3]  [ 2 ] 李哲,庹先国,穆克亮,钟红梅.矿样中钛铁EDXRF分析的基体效应和神经网络校正研究[J].核技术,2009,32(1):35
[4]  LI Zhe, TUO Xianguo, MU Keliang, ZHONG Hongmei. Matrix effect and ANN correcting technique in EDXRF analysis of Ti and Fe in core samples [J].Nuclear Techniques, 2009,32(1):35
[5]  [ 3 ] 曹利国.X射线荧光分析中的综合灵敏度因子KI0和准绝对测量[J].核技术,1987,10(7):15
[6]  CAO Liguo. Comprehensive sensitivity factor KI0 and quasi-absolute determination in XRF [J].Nuclear Techniques,1987,10(7):15
[7]  [ 4 ] REN Jun, LIU Mingzhe, TUO Xianguo, LI Zhe, SHI Rui. Towards a hybrid optimization model for elemental content analysis in EDXRF[C].Proceedings - International Conference on Natural Computation, 2012,1251-1254
[8]  CAO Liguo. Comprehensive sensitivity factor KI0 and quasi-absolute determination in XRF [J].Nuclear Techniques,1987,10(7):15
[9]  [ 4 ] REN Jun, LIU Mingzhe, TUO Xianguo, LI Zhe, SHI Rui. Towards a hybrid optimization model for elemental content analysis in EDXRF[C].Proceedings - International Conference on Natural Computation, 2012,1251-1254
[10]  [ 8 ] Guo, X.C., Yang, J.H., Wu, C.G., Wang, C.Y., Liang, Y.C. A novel LS-SVMs hyper-parameter selection based on particle swarm optimization [J] .Neurocomputing, 2008, 71:3211
[11]  [ 9 ] Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.. Choosing multiple parameters for support vector machines [J].Machine Learning, 2009, 46: 131
[12]  [ 10 ] Wang, H., He, Z.. A short-term load forecasting immune support vector machines [J].Power System Technology, 2004, 23:12
[13]  [ 5 ] Wen-Tsao Pan. A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example[j].Knowledge-Based Systems, 2012, 26:69
[14]  [ 6 ] Suykens J A K, Gestel T V, Brabanter J D, et al. Least Squares Support Vector Machines[J].Singapore: World Scientific Publishing, 2002
[15]  [ 7 ] 顾燕萍,赵文杰,吴占松. 最小二乘支持向量机的算法研究[J].清华大学学报(自然科学版),2010,50(7):1063
[16]  [ 5 ] Wen-Tsao Pan. A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example[j].Knowledge-Based Systems, 2012, 26:69
[17]  [ 6 ] Suykens J A K, Gestel T V, Brabanter J D, et al. Least Squares Support Vector Machines[J].Singapore: World Scientific Publishing, 2002
[18]  [ 7 ] 顾燕萍,赵文杰,吴占松. 最小二乘支持向量机的算法研究[J].清华大学学报(自然科学版),2010,50(7):1063
[19]  GU Yan-ping, ZHAO Wen-jie, WU Zhan-song..Least squares support vector machine algorithm[J].Journal of Tsinghua University (Science and Technology),2010,50(7):1063
[20]  [ 8 ] Guo, X.C., Yang, J.H., Wu, C.G., Wang, C.Y., Liang, Y.C. A novel LS-SVMs hyper-parameter selection based on particle swarm optimization [J] .Neurocomputing, 2008, 71:3211
[21]  [ 9 ] Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.. Choosing multiple parameters for support vector machines [J].Machine Learning, 2009, 46: 131
[22]  [ 10 ] Wang, H., He, Z.. A short-term load forecasting immune support vector machines [J].Power System Technology, 2004, 23:12
[23]  [ 11 ] Pourbasheer, E., Riahi, S., Ganjali, M.R., Norouzi, P.. Application of genetic algorithm support vector machine (GA-SVM) for prediction of BK-channels activity [J]. European Journal of Medicinal Chemistry, 2009,44:5023
[24]  [ 12 ] LIU Mingzhe, TUO Xianguo, REN Jun, LI Zhe, WANG Lei, YANG Jianbo. A PSO-SVM based model for alpha particle activity prediction inside decommissioned channels [J].Lecture Note in Computer Science 7367LNGS, 2012,517
[25]  GU Yan-ping, ZHAO Wen-jie, WU Zhan-song..Least squares support vector machine algorithm[J].Journal of Tsinghua University (Science and Technology),2010,50(7):1063
[26]  [ 8 ] Guo, X.C., Yang, J.H., Wu, C.G., Wang, C.Y., Liang, Y.C. A novel LS-SVMs hyper-parameter selection based on particle swarm optimization [J] .Neurocomputing, 2008, 71:3211
[27]  [ 9 ] Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.. Choosing multiple parameters for support vector machines [J].Machine Learning, 2009, 46: 131
[28]  [ 10 ] Wang, H., He, Z.. A short-term load forecasting immune support vector machines [J].Power System Technology, 2004, 23:12
[29]  [ 11 ] Pourbasheer, E., Riahi, S., Ganjali, M.R., Norouzi, P.. Application of genetic algorithm support vector machine (GA-SVM) for prediction of BK-channels activity [J]. European Journal of Medicinal Chemistry, 2009,44:5023
[30]  [ 12 ] LIU Mingzhe, TUO Xianguo, REN Jun, LI Zhe, WANG Lei, YANG Jianbo. A PSO-SVM based model for alpha particle activity prediction inside decommissioned channels [J].Lecture Note in Computer Science 7367LNGS, 2012,517
[31]  [ 11 ] Pourbasheer, E., Riahi, S., Ganjali, M.R., Norouzi, P.. Application of genetic algorithm support vector machine (GA-SVM) for prediction of BK-channels activity [J]. European Journal of Medicinal Chemistry, 2009,44:5023
[32]  [ 12 ] LIU Mingzhe, TUO Xianguo, REN Jun, LI Zhe, WANG Lei, YANG Jianbo. A PSO-SVM based model for alpha particle activity prediction inside decommissioned channels [J].Lecture Note in Computer Science 7367LNGS, 2012,517

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