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论文类型：期刊论文

发表时间：2016-01-01

发表刊物：CONTROL ENGINEERING PRACTICE

收录刊物：SCIE、EI

卷号：46

页面范围：94-104

ISSN号：0967-0661

关键字：BFG generation; High level noises and outliers; Echo state networks; Quantile regression; Prediction intervals

摘要：The usual huge fluctuations in the blast furnace gas (BFG) generation make the scheduling of the gas system become a difficult problem. Considering that there are high level noises and outliers mixed in original industrial data, a quantile regression-based echo state network ensemble (QR-ESNE) is modeled to construct the prediction intervals (PIs) of the BFG generation. In the process of network training, a linear regression model of the output matrix is reported by the proposed quantile regression to improve the generalization ability. Then, in view of the practical demands on reliability and further improving the prediction accuracy, a bootstrap strategy based on QR-ESN is designed to construct the confidence intervals and the prediction ones via combining with the regression models of various quantiles. To verify the performance of the proposed method, the practical data coming from a steel plant are employed, and the results indicate that the proposed method exhibits high accuracy and reliability for the industrial data. Furthermore, an application software system based on the proposed method is developed and applied to the practice of this plant. (c) 2015 Elsevier Ltd. All rights reserved.