用支持向量机预测中药水提液膜分离过程
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摘要
为了找出中药水提液膜过程中影响膜污染的主要原因和预测膜污染的程度以防止膜污染,研究用支持向量机分类、遗传神经网络于中药水提液膜中属性筛选。以筛选出的主要属性用支持向量机回归建模预测,讨论确定模型参数、模型优化等关键问题,并与神经网络运行结果对比分析。分析结果表明支持向量机回归算法对膜污染度的拟合效果和预测能力均好于对该问题分析的其他方法。
With the purpose of finding the critical factors which exert great influences on membrane pollution in the filtration of Chinese medicine' s water extract and preventing membrane pollution by forecasting the pollution degree,we applied support vector classification (SVC) and genetic neural network to select main features,then we applied support vector regression(SVR) with main features to build model and forecast the pollution degree in the process.In addition,parameter selection and model optimization as key issues were fully discussed in this study.Meantime,model built from SVR was compared with artificial neutral network(ANN),thus it can be concluded that SVR is superior to other algorithms in forecasting and fitting membrane pollution degree.
With the purpose of finding the critical factors which exert great influences on membrane pollution in the filtration of Chinese medicine' s water extract and preventing membrane pollution by forecasting the pollution degree,we applied support vector classification (SVC) and genetic neural network to select main features,then we applied support vector regression(SVR) with main features to build model and forecast the pollution degree in the process.In addition,parameter selection and model optimization as key issues were fully discussed in this study.Meantime,model built from SVR was compared with artificial neutral network(ANN),thus it can be concluded that SVR is superior to other algorithms in forecasting and fitting membrane pollution degree.
引文
1 Wang Beiying,Wang Yuesheng and Wang Huankui.High-tech urgently needed in Chinese pharmaceutical industry.The World of Science and Technology,2001,2(2):18-24.
2 Siskens C A M.Applicattion of ceramic membranes in liquid filtration: 619-639//Fundamental of Inorganic Membrane Science and Technology,Burggraaf A J and Cot,L editors,Elsevier Science BV,1996.
3 Huan Zhongtao,et al.Inorganic Membrane Technology and its Applications. Beijing:China Petrochemical Press,1999:250-306.
4 Sheikonholeslami R.Fouling mitigation in membrane processes.Desalination, 1999,123(1):45-53.
5 Marques J P de Sa.Cheng Yifei Translated.Pattern Recognition-Concepts, Methods and Applications.Beijing:Tsinghua University Press,2002:140-222.
6 Zhen Quan,Cheng Jingxian and Cheng Liqing.Product quality design optimization based on genetic algorithm.Design and Research, 2008,4:9-11.
7 Qi Jun,et al.Research on QSPR for n-octanol-water partition coefficients of organic compounds based on genetic algorithms-support vector machine and genetic algorithms-radial basis function neural networks.Environmental Science,2008,29(1):212-218.
8 Chen Yichao,Zeng Sanyou,Zhang Haochun and Quan Furong.Research on earthquake prediction based on genetic neural networks. Jisuanji Yingyong yu Ruanjian,2008,25(4):135-137.
9 Vapnik V.Statistical Searning Theory.New York:Wiley,1998.
10 Yongseog Kim,W.Nick Street and Filippo Menczer.Featuer Selection in Data Mining.University of Iowa,USA.
11 Cristianini N,Shawe-Taylor J and Li Guozheng Translated.An Introduction to Support Vector Machines.Beijing:Earthquake Press, 1998.
12 Zhang Xuegong.Introduction to statistical learning theory and support vector machines.ACTA Automatica SINICA,2000,26(1):32 -26.
13 Yang Jie,Ye Chenzhou,Quan Yong and Chen Nianyi.Simplified SMO algorithm for support vector regression.Infrared and Laser Engineering, 2004,33(5):533-537.
14 Deng Naiyang and Tian Yingjie.A New Method in Data Mining-Support Vector Machine.Beijing:Science Press,2004.
15 Cui Xiujun,Wang Zhixin,Yuan Xing and Zhang Zhuoyong.Application support vector machine to QSAR study of toxicity of substituted phenols.Computers and Applied Chemistry,2008,25(3):298 -302.
16 Yang Zhen,Gu Xinsheng,Liang Xiaoyi and Ling Licheng.Study and application of pattern recognition and support vector machines in composite optimized design.Computers and Applied Chemistry, 2008,25(1):31-34.
17 Chen Nianyi,Lu Wencong,Ye Chenzhou and Li Guozheng.Application of support vector machine and kernel function in chemomettics. Computer and Applied Chemistry,2002,19(6):691-696.
18 Guo Liwei,Dong Jie and Fan Wenling.The research of data mining used in optimization of the filtration of chinese herbal medicine. Modernization of Traditional Chinese Medicine,2005,7(3):42- 47.
19 Zhu Shuxian and Zhang Renjie.Research for selection of kernel functions used in support vector machine.Science Technology and Engineering,2008,8(16):4513-4517.
20 Chang Chinchung and Lin Chinjen.LIBSVM:a library for support vector machines,2001.Software available at http://www.csie. ntu.edu.tw/～cjlin/libsvm.
21 Lin Shengliang and Liu Zhi.Parameter selection in SVM with RBF kernel function.Journal of Zhejiang University of Technology, 2007,35(2):163-167.
22 Xue Zhenxia,Huang Yanhui and Zhang Suling.Max_min method for kernel-parameter selection in SVM.Henan Science,2007,25 (3):469-472.
23 Flake Gary William and Lawrence Steve.Efficient SVM regression training with SMO.Machine Learning,2002,13(5):1024-1027.
24 Vapnik V.The Nature of Statistical Learning Theory.2nd ed.New York:Springer Verlag,1998.
25 Platt J C.Fast training of support vector machines using sequential minimal optimization.Microsoft Research,http://www.research. microsoft.com/～jplatt.
1王北婴,王跃生,王焕魁,等.我国中药制药工业中亟需推广的高新技术[J].世界科学技术,2001,2(2):18-24.
3黄仲涛,等.无机膜技术及其应用[M].北京:中国石化出版社,1999:250-306.
5 Marques de Sa JP.模式识别——原理、方法及应用[M].陈逸飞,译.北京:清华大学出版社,2002:140-222.
6郑泉,陈敬贤,陈黎卿.基于遗传算法的产品质量设计优化[J].设计与研究,2008(4):9-11.
7齐珺,等.基于遗传-支持向量机和遗传-径向基网络的有机物正辛醇-水分配系数QSPR研究[J].环境科学,2008,29(1): 212-218.
8陈一超,等.基于遗传神经网络的地震预测研究[J].计算机应用与软件,2008,25(4):135-137.
11李国正,等译.支持向量机导论[M].北京:地震出版社,1998.
12张学工.关于统计学习理论与支持向量机[J].自动化学报, 2000,26(1):32-42.
13杨杰,叶晨洲,全勇,陈念贻.支持向量机回归的SMO算法[J].红外与激光工程,2004,33(5):533-536.
14邓乃杨,田硬杰.数据挖掘中的新方法——支持向量机[M].北京:科学出版社,2004.
15崔秀君,王志欣,袁星,张卓勇.支持向量机用于酚类化合物毒性的QSAR研究[J].计算机与应用化学,2008,25(3):298 -302.
16杨榛,顾幸生,梁晓怿,凌立成.模式识别和支持向量机在复合材料优化设计中的研究和应用[J].计算机与应用化学, 2008,(1):31-34.
17陈念贻,陆文聪,叶晨洲,李国正.支持向量机及其他核函数算法在化学计量学中的应用[J].计算机与应用化学,2002,19 (6):691-696.
18郭立玮,董洁,樊文玲.数据挖掘方法用于中药水提液膜过程优化的研究[J].中医药现代化,2005,7(3):42-47.
19朱树先,张仁杰.支持向量核函数选择的研究[J].科学技术与工程,2008,8(16):4513-4517.
21林升梁,刘志.基于RBF核函数的支持向量机参数选择[J].浙江工业大学学报,2007,35(2):163-167.
22薛贞霞,黄彦辉,张素玲.支持向量机核参数选择的MAX-MIN方法[J].河南科学,2007,25(3):469-472.
2 Siskens C A M.Applicattion of ceramic membranes in liquid filtration: 619-639//Fundamental of Inorganic Membrane Science and Technology,Burggraaf A J and Cot,L editors,Elsevier Science BV,1996.
3 Huan Zhongtao,et al.Inorganic Membrane Technology and its Applications. Beijing:China Petrochemical Press,1999:250-306.
4 Sheikonholeslami R.Fouling mitigation in membrane processes.Desalination, 1999,123(1):45-53.
5 Marques J P de Sa.Cheng Yifei Translated.Pattern Recognition-Concepts, Methods and Applications.Beijing:Tsinghua University Press,2002:140-222.
6 Zhen Quan,Cheng Jingxian and Cheng Liqing.Product quality design optimization based on genetic algorithm.Design and Research, 2008,4:9-11.
7 Qi Jun,et al.Research on QSPR for n-octanol-water partition coefficients of organic compounds based on genetic algorithms-support vector machine and genetic algorithms-radial basis function neural networks.Environmental Science,2008,29(1):212-218.
8 Chen Yichao,Zeng Sanyou,Zhang Haochun and Quan Furong.Research on earthquake prediction based on genetic neural networks. Jisuanji Yingyong yu Ruanjian,2008,25(4):135-137.
9 Vapnik V.Statistical Searning Theory.New York:Wiley,1998.
10 Yongseog Kim,W.Nick Street and Filippo Menczer.Featuer Selection in Data Mining.University of Iowa,USA.
11 Cristianini N,Shawe-Taylor J and Li Guozheng Translated.An Introduction to Support Vector Machines.Beijing:Earthquake Press, 1998.
12 Zhang Xuegong.Introduction to statistical learning theory and support vector machines.ACTA Automatica SINICA,2000,26(1):32 -26.
13 Yang Jie,Ye Chenzhou,Quan Yong and Chen Nianyi.Simplified SMO algorithm for support vector regression.Infrared and Laser Engineering, 2004,33(5):533-537.
14 Deng Naiyang and Tian Yingjie.A New Method in Data Mining-Support Vector Machine.Beijing:Science Press,2004.
15 Cui Xiujun,Wang Zhixin,Yuan Xing and Zhang Zhuoyong.Application support vector machine to QSAR study of toxicity of substituted phenols.Computers and Applied Chemistry,2008,25(3):298 -302.
16 Yang Zhen,Gu Xinsheng,Liang Xiaoyi and Ling Licheng.Study and application of pattern recognition and support vector machines in composite optimized design.Computers and Applied Chemistry, 2008,25(1):31-34.
17 Chen Nianyi,Lu Wencong,Ye Chenzhou and Li Guozheng.Application of support vector machine and kernel function in chemomettics. Computer and Applied Chemistry,2002,19(6):691-696.
18 Guo Liwei,Dong Jie and Fan Wenling.The research of data mining used in optimization of the filtration of chinese herbal medicine. Modernization of Traditional Chinese Medicine,2005,7(3):42- 47.
19 Zhu Shuxian and Zhang Renjie.Research for selection of kernel functions used in support vector machine.Science Technology and Engineering,2008,8(16):4513-4517.
20 Chang Chinchung and Lin Chinjen.LIBSVM:a library for support vector machines,2001.Software available at http://www.csie. ntu.edu.tw/～cjlin/libsvm.
21 Lin Shengliang and Liu Zhi.Parameter selection in SVM with RBF kernel function.Journal of Zhejiang University of Technology, 2007,35(2):163-167.
22 Xue Zhenxia,Huang Yanhui and Zhang Suling.Max_min method for kernel-parameter selection in SVM.Henan Science,2007,25 (3):469-472.
23 Flake Gary William and Lawrence Steve.Efficient SVM regression training with SMO.Machine Learning,2002,13(5):1024-1027.
24 Vapnik V.The Nature of Statistical Learning Theory.2nd ed.New York:Springer Verlag,1998.
25 Platt J C.Fast training of support vector machines using sequential minimal optimization.Microsoft Research,http://www.research. microsoft.com/～jplatt.
1王北婴,王跃生,王焕魁,等.我国中药制药工业中亟需推广的高新技术[J].世界科学技术,2001,2(2):18-24.
3黄仲涛,等.无机膜技术及其应用[M].北京:中国石化出版社,1999:250-306.
5 Marques de Sa JP.模式识别——原理、方法及应用[M].陈逸飞,译.北京:清华大学出版社,2002:140-222.
6郑泉,陈敬贤,陈黎卿.基于遗传算法的产品质量设计优化[J].设计与研究,2008(4):9-11.
7齐珺,等.基于遗传-支持向量机和遗传-径向基网络的有机物正辛醇-水分配系数QSPR研究[J].环境科学,2008,29(1): 212-218.
8陈一超,等.基于遗传神经网络的地震预测研究[J].计算机应用与软件,2008,25(4):135-137.
11李国正,等译.支持向量机导论[M].北京:地震出版社,1998.
12张学工.关于统计学习理论与支持向量机[J].自动化学报, 2000,26(1):32-42.
13杨杰,叶晨洲,全勇,陈念贻.支持向量机回归的SMO算法[J].红外与激光工程,2004,33(5):533-536.
14邓乃杨,田硬杰.数据挖掘中的新方法——支持向量机[M].北京:科学出版社,2004.
15崔秀君,王志欣,袁星,张卓勇.支持向量机用于酚类化合物毒性的QSAR研究[J].计算机与应用化学,2008,25(3):298 -302.
16杨榛,顾幸生,梁晓怿,凌立成.模式识别和支持向量机在复合材料优化设计中的研究和应用[J].计算机与应用化学, 2008,(1):31-34.
17陈念贻,陆文聪,叶晨洲,李国正.支持向量机及其他核函数算法在化学计量学中的应用[J].计算机与应用化学,2002,19 (6):691-696.
18郭立玮,董洁,樊文玲.数据挖掘方法用于中药水提液膜过程优化的研究[J].中医药现代化,2005,7(3):42-47.
19朱树先,张仁杰.支持向量核函数选择的研究[J].科学技术与工程,2008,8(16):4513-4517.
21林升梁,刘志.基于RBF核函数的支持向量机参数选择[J].浙江工业大学学报,2007,35(2):163-167.
22薛贞霞,黄彦辉,张素玲.支持向量机核参数选择的MAX-MIN方法[J].河南科学,2007,25(3):469-472.
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