|本期目录/Table of Contents|

[1]董维振,陈 燕*,李媛媛.基于多元逐步回归的带钢性能预测模型[J].工业仪表与自动化装置,2022,(02):107-111.[doi:10.19950/j.cnki.cn61-1121/th.2022.02.022]
 DONG Weizhen,CHEN Yan*,LI Yuanyuan.Research on prediction model of steel properties based on multiple stepwise regression and data mining[J].Industrial Instrumentation & Automation,2022,(02):107-111.[doi:10.19950/j.cnki.cn61-1121/th.2022.02.022]
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基于多元逐步回归的带钢性能预测模型

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2022年02期
页码:
107-111
栏目:
出版日期:
2022-04-15

文章信息/Info

Title:
Research on prediction model of steel properties based on multiple stepwise regression and data mining
文章编号:
1000-0682(2022)02-0000-00
作者:
董维振1陈 燕1*李媛媛2
(1.广西大学 计算机与电子信息学院,广西 南宁 530004;2.曹妃甸职业技术学院 人工智能学院,河北 唐山 063205)
Author(s):
DONG Weizhen1CHEN Yan1*LI Yuanyuan2
( 1.School of Computer,Electronics and Information, Guangxi University, Guangxi Nanning 530004, China;2.College of artificial intelligence,CAOFEIDIAN COLLEGE OF TECHOLOGY, Hebei Tangshan 063205, China)
关键词:
带钢性能预测机器学习数据挖掘多元线性回归逐步回归大数据
Keywords:
strip performance prediction machine learning data mining multiple linear regression stepwise regression big data
分类号:
TP391.41
DOI:
10.19950/j.cnki.cn61-1121/th.2022.02.022
文献标志码:
A
摘要:
为了挖掘冶金产品关键工艺参数与成品性能间的关系、实现特定牌号性能自动预测,构建了带钢性能预测模型。模型基于多元线性回归的机器学习方法和数据挖掘方法,构建了牌号性能影响因素知识库,通过大量真实生产环境数据,分析其相关性和性能影响因素。实验过程基于2020个预测样本的数据集,其中选择1616个样本作为训练集,404个样本作为测试集,每个样本包括24 种属性,对成品性能Y1和Y2进行了单独训练和预测,均方差结果仅为0.018 2和2.937 1×10-5。实验表明,该性能预测模型预测准确率高,有效命中率达到90%以上,具有良好的应用前景。
Abstract:
In order to explore the relationship between the key process parameters of metallurgical products and the properties of finished products and realize the automatic prediction of the properties of specific grades, a strip performance prediction model was constructed. Based on the machine learning method and data mining method of multiple linear regression, the model constructs the knowledge base of brand performance influencing factors, and analyzes its correlation and performance influencing factors through a large number of real production environment data. The experimental process is based on the data set of 2020 prediction samples, of which 1616 samples are selected as the training set and 404 samples as the test set. Each sample includes 24 attributes. The finished product performance Y1 and Y2 are trained and predicted separately, and the mean square deviation results are only 0.018 2 and 2.937 1 × 10-5。 Experiments show that the prediction accuracy of the performance prediction model is high, and the effective hit rate is more than 90%, which has a good application prospect.

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备注/Memo

备注/Memo:
收稿日期:2021-11-06

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更新日期/Last Update: 1900-01-01