计算机工程与应用2019，Vol.55Issue(12)：250-258,9.DOI:10.3778/j.issn.1002-8331.1712-0175
铝电解关键指标预测方法的研究与应用
Research and Application of Prediction Method for Key Indexes of Aluminum Electrolysis
摘要
Abstract
This 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. Firstly, 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 algo-rithm classification. By using single slot test set data, every index level can be predicted by accumulating method, com-pared with the previous day, the change of variables are confirmed under each index, 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.关键词
铝生产/模糊聚类/朴素贝叶斯/累积法/增量思想Key words
aluminum production/ fuzzy clustering/ naive Bayes/ cumulative method/ incremental thinking分类
信息技术与安全科学引用本文复制引用
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):250-258,9.基金项目
中国科学院科技服务网络计划项目(No.Y7AA050A01). （No.Y7AA050A01）
