论文类型：期刊论文

第一作者：Zhao J.

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：Zhang X.,Wang W.

发表时间：2010-12-01

发表刊物：ICIC Express Letters

收录刊物：EI、Scopus

文献类型：J

卷号：4

期号：6 A

页面范围：2069-2074

ISSN号：1881803X

摘要：The accurate prediction to the gas amount consumed by hot blast furnace (HBF) is one of the important tasks for the gas scheduling in steel industry. In this study, a prediction model based on least support vector machine (LSSVM) is developed by considering the characteristics of gas consumption flow and the on-line requirement in practice. Since it is difficult to determine the optimal parameters of the model by some common methods, a fast cross-validation (FCV) method combined with the gradient based optimal algorithm is proposed in this paper to realize the on-line optimization to the hyper parameters. The simulation results with the practical blast furnace gas consumption data of HBF in Shanghai Baosteel show the modeling method is time-economized, while the on-line prediction performance are superior to the standard SVM and the neural network. ICIC International ? 2010 ISSN 1881-803X.

是否译文：否