多維時序模糊關聯規則在高爐爐溫預報中的應用

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基金項目:

國家經濟貿易委員會資助項目(No.02BK-101-8)

Application of multidimensional time series fuzzy association rules for hot metal temperature forecasting in a blast furnace

1. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China;
2. School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China;
3. Wuhan Iron and Steel Group Corporation, Wuhan 430083, China

摘要: 根據目前高爐爐溫預報推理規則都是由高爐專家根據經驗制定的情況,提出了一種新的規則生成方法——數據挖掘獲取高爐爐溫預報關聯規則.針對現有挖掘算法的不足,提出了一種改進的多維時間序列模糊關聯規則挖掘算法,該算法基于時間子序列和子序列間隔的雙重模糊化,避免了挖掘結果"時間邊界銳化"的問題.該算法應用于武鋼的1^#高爐,挖掘效果良好.
- 高爐 /
- 專家系統 /
- 爐溫預報 /
- 模糊關聯規則 /
- 時間序列
Abstract: Confronted by the present state that the rules for hot metal temperature forecasting are made merely on the base of the experience of blast furnace (BF) experts, a new approach to the rules established through association rules mining from BF data was put forward. The algorithm of multidimensional time series rules mining was improved. The improved algorithm, which bases on the fuzziness of both subsequence and suhsequence interval, avoids the influence of "time border sharpness" on the result of mining. The algorithm was applied to No. 1 BF at Wuhan Iron and Steel Group Corporation, and its effects turned out to be satisfactory.
- blast furnace（BF） /
- expert system /
- hot metal temperature forecast /
- fuzzy association rules /
- time series