论文类型：期刊论文

第一作者：Zhang X.

通讯作者：Zhao, J.; Research Center of Information and Control, Dalian University of Technology, Dalian 116024, China; email: zhaoj@dlut.edu.cn

合写作者：Zhao J.,Wang W.

发表时间：2010-10-01

发表刊物：ICIC Express Letters

收录刊物：EI、Scopus

文献类型：J

卷号：4

期号：5 B

页面范围：1767-1772

ISSN号：1881803X

摘要：The key of gas scheduling in modern steel enterprises is to accurately predict gasholder level on-line. Because of the frequent and great fluctuation of level, it is difficult to be precisely predicted by the manual experience or the mechanism model based method. In this study, a class of MKL based on reduced gradient algorithm is extended using LSSVR, and the MKLLSSVR is developed. The MKLLSSVR overcomes the poor generalization of single kernel based LSSVR and the long training process of traditional MKL based SVR, and can rapidly give the optimal linear combination of kernels to base the resulting regressor for better interpretation. The simulation results with the real gasholder level data show the MKLLSSVR can achieve better prediction performance compared to the traditional MKL and single kernel based LSSVR. ? 2010 ICIC International.

是否译文：否