Modeling of Isomerization of C_8 Aromatics by Online Least Squares Support Vector Machine  

在线最小二乘支持向量机及其在C_8芳烃异构化建模中的应用(英文)

作　　者：李丽娟 苏宏业 褚建 

机构地区：[1]College of Automation and Electrical Engineering,Nanjing University of Technology [2]State Key Lab. of Industrial Control Technology, Institute of Cyber-systems and Control,Zhejiang University

出　　处：《Chinese Journal of Chemical Engineering》2009年第3期437-444,共8页中国化学工程学报（英文版）

基　　金：Supported by the National Creative Research Groups Science Foundation of China (60721062);the National Basic Research Program of China (2007CB714000)

摘　　要：The least squares support vector regression (LS-SVR) is usually used for the modeling of single output system, but it is not well suitable for the actual multi-input-multi-output system. The paper aims at the modeling of multi-output systems by LS-SVR. The multi-output LS-SVR is derived in detail. To avoid the inversion of large matrix, the recursive algorithm of the parameters is given, which makes the online algorithm of LS-SVR practical. Since the computing time increases with the number of training samples, the sparseness is studied based on the pro-jection of online LS-SVR. The residual of projection less than a threshold is omitted, so that a lot of samples are kept out of the training set and the sparseness is obtained. The standard LS-SVR, nonsparse online LS-SVR and sparse online LS-SVR with different threshold are used for modeling the isomerization of C8 aromatics. The root-mean-square-error (RMSE), number of support vectors and running time of three algorithms are compared and the result indicates that the performance of sparse online LS-SVR is more favorable.The least squares support vector regression （LS-SVR） is usually used for the modeling of single output system, but it is not well suitable for the actual multi-input-multi-output system. The paper aims at the modeling of multi-output systems by LS-SVR. The multi-output LS-SVR is derived in detail. To avoid the inversion of large matrix, the recursive algorithm of the parameters is given, which makes the online algorithm of LS-SVR practical. Since the computing time increases with the number of training samples, the sparseness is studied based on the pro-jection of online LS-SVR. The residual of projection less than a threshold is omitted, so that a lot of samples are kept out of the training set and the sparseness is obtained. The standard LS-SVR, nonsparse online LS-SVR and sparse online LS-SVR with different threshold are used for modeling the isomerization of C8 aromatics. The root-mean-square-error （RMSE）, number of support vectors and running time of three algorithms are compared and the result indicates that the performance of sparse online LS-SVR is more favorable.

关 键 词：least squares support vector machine multi-variable ONLINE SPARSENESS ISOMERIZATION 

分 类 号：O621.1[理学—有机化学]