Molten steel temperature prediction model based on bootstrap Feature Subsets Ensemble Regression Trees
Molten steel temperature prediction model based on bootstrap Feature Subsets Ensemble Regression Trees
复制标题
基于Bootstrap特征子集集成回归树的钢水温度预测模型
DOI：
10.1016/j.knosys.2016.02.018
复制
发表时间：
2016-06
影响因子：
8.8
通讯作者：
You Mingshuang
中科院分区：
文献类型：
--
作者：
Wang Xiaojun;Yuan Ping;Mao Zhizhong;You Mingshuang
钢水温度预报是钢包炉生产中的重要环节.大多数现有的温度模型都是建立在小尺度数据上的。这些模型的精度和泛化能力不能满足工业生产的需要。现在，从生产过程中积累了大量的数据，其中包含了更多有用的信息。然而，这些数据是有噪声的。大规模和噪声数据对建立温度模型施加了很大的限制。为了解决这两个问题，本文提出了Bootstrap特征子集包围回归树（BFSE-RTs）方法。首先，基于多元模糊泰勒定理构造低维特征子集，这节省了计算机中的存储空间，并表示使用“小规模”数据集。其次，为了消除噪声，独立同分布数据的自助抽样方法被应用到特征子集。Bootstrap复制由较小规模和低维样本组成。第三，考虑到它的简单性，回归树（RT）是建立在每个引导复制。最后，通过分析LF炉的热过程，采用BFSE-RTs方法建立了LF炉的温度模型。实验结果表明，BFSE-RTs方法优于其他估计方法，提高了精度和泛化能力，满足RMSE和最大误差对温度预测的要求。
Molten steel temperature prediction is important in Ladle Furnace (LF). Most of the existing temperature models have been built on small-scale data. The accuracy and the generalization of these models cannot satisfy industrial production. Now, the large-scale data with more useful information are accumulated from the production process. However, the data are with noise. Large-scale and noise data impose strong restrictions on building a temperature model. To solve these two issues, the Bootstrap Feature Subsets Ensemble Regression Trees (BFSE-RTs) method is proposed in this paper. Firstly, low-dimensional feature subsets are constructed based on the multivariate fuzzy Taylor theorem, which saves more memory space in computers and indicates ``smaller-scale'' data sets are used. Secondly, to eliminate the noise, the bootstrap sampling approach of the independent identically distributed data is applied to the feature subsets. Bootstrap replications consist of smaller-scale and lower-dimensional samples. Thirdly, considering its simplicity, a Regression Tree (RT) is built on each bootstrap replication. Lastly, the BFSE-RTs method is used to establish a temperature model by analyzing the metallurgic process of LF. Experiments demonstrate that the BFSE-RTs outperforms other estimators, improves the accuracy and the generalization, and meets the requirements of the RMSE and the maximum error on the temperature prediction.
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DOI：
10.1007/978-0-387-75692-9_9
发表时间：
2008
期刊：
--
影响因子：
--
作者：
D. Hinkley
通讯作者：
D. Hinkley
影响因子：
7.5
作者：
L. Breiman
通讯作者：
L. Breiman
影响因子：
8.8
作者：
Chunyun Zhang;Weiran Xu;Zhanyu Ma;Sheng Gao;Qun Li;Jun Guo
通讯作者：
Jun Guo
DOI：
10.3115/1687878.1687935
发表时间：
2009-08
期刊：
--
影响因子：
--
作者：
Tara McIntosh;J. Curran
通讯作者：
Tara McIntosh;J. Curran
影响因子：
7.5
作者：
C. Merz;M. Pazzani
通讯作者：
C. Merz;M. Pazzani