Processing of spectrophotometric array signals using an artificial intelligence method

摘要：

　　This paper addresses processing of spectrophotometric array signals based on genetic algorithms (GA) least square support vector machines (LS-SVM)regression to provide a powerful model for machine learning and data mining.The key to complete LS-SVM regression is to choose its optimal parameters.Due to their outstanding ability in solving global optimization problems in complex multidimensional search space,GA are used in this study to obtain the optimal parameter combination of the LS-SVM model.Experimental results showed the GA-LS-SVM method to be successful for simultaneous multieomponent determination even where severe overlap of spectra was present.

关键词： Least squares support vector machines Genetic algorithms Spectrophotometric array signals Overlapping spectra Artificial intelligence

作者: Ling Gao Shouxin Ren

作者单位: Department of Chemistry Inner Mongolia University Huhhot,China

会议类型: 国际会议

会议名称: 2013 2nd International Conference on Computer Science and Electronics Engineering(ICCSEE2013)(2013年第二届计算机科学与电子工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 1244-1247

在线出版日期: 2013-03-22（万方平台首次上网日期，不代表论文的发表时间）