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Alternative TitleResearch and application of prediction method for key indexes of aluminum electrolysis.
陈勇1,2,3; 周晓锋1,3; 李帅1,2,3
Source Publication计算机工程与应用
Indexed ByCSCD
Contribution Rank1
Funding Organization中国科学院科技服务网络计划项目(No.Y7AA050A01)
Keyword铝生产 模糊聚类 朴素贝叶斯 累积法 增量思想
Other AbstractThis paper presents a strategy to maintain the stability of aluminum reduction cells through the data mining and modeling of the aluminum factory in order to maintain the continuity of aluminum electrolytic production and ensure the stability of energy consumption and the stability of energy consumption. First, De-noising for data. In view of the unknown data distribution characteristics of aluminum plants, a fuzzy clustering method without parameter adaptation is proposed, and the number and cluster centers of clusters can be obtained through iterative adaptation. According to the clustering results, the data of aluminum factory is labelled according to the actual meaning. A distance based continuous attribute naive Bias algorithm is proposed. The incremental idea of classifiers is used to improve the accuracy of algorithm classification. By using single slot test set data, we can predict every index level by accumulating method, confirm the change of variables under each index compared with the previous day, and then complete the prediction. It is found that the prediction model can predict the key indicators of aluminum electrolysis, and the proposed clustering and classification algorithms are good in UCI data and aluminum factory data.
Citation statistics
Document Type期刊论文
Corresponding Author陈勇
Recommended Citation
GB/T 7714
陈勇,周晓锋,李帅. 铝电解关键指标预测方法的研究与应用[J]. 计算机工程与应用,2019,55(12):250-258.
APA 陈勇,周晓锋,&李帅.(2019).铝电解关键指标预测方法的研究与应用.计算机工程与应用,55(12),250-258.
MLA 陈勇,et al."铝电解关键指标预测方法的研究与应用".计算机工程与应用 55.12(2019):250-258.
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