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论文类型：期刊论文

发表时间：2012-11-01

发表刊物：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录刊物：SCIE、EI、Scopus

卷号：8

期号：4

页面范围：953-963

ISSN号：1551-3203

关键字：Byproduct gas; hyperparameter optimization; least square support vector machine; noise estimation; prediction

摘要：A rapid and accurate prediction of byproduct gas flow in steel industry can help not only to become aware of the operational situations of gas system, but it also provides the energy scheduling workers with sound decision-making mechanisms. In this study, a least square support vector machine (LS-SVM) model based on online hyperparameters optimization is proposed, where the variance of effective noise of the sample is estimated, while a conjugate gradient algorithm is developed to optimize the width of Gaussian kernels and the regularization factor. To assess the quality of the proposed method, we experiment with a test function affected by additive noise and an industrial gas flow data from Shanghai Baosteel Company Ltd. A series of comparative experiments are reported as well. The results demonstrate that the proposed method shows the shortest computing time while ensuring the prediction accuracy. These two features make the approach applicable to real-time prediction of gas flow in steel industry.