摘要
文章分析了煤化工企业过程数据的特性,以及不同方法对过程数据缺失填补的优劣性,提出了一种基于相关性因子的工业缺失数据填补方法。通过引入相关因子,对传统的时间序列法进行改进,提高了对工业缺失数据估计的准确性。实验结果验证了论文方法的有效性。
This paper analyzes the characteristics of process data in coal chemical enterprises and the effect of different methods for filling in the lack of process data, and puts forward an industrial missing data filling method based on correlation factor. By introducing correlation factors, the traditional time series method is improved to improve the accuracy of industrial missing data estimation. The experimental results verify the effectiveness of the proposed method.
出处
《自动化博览》
2021年第12期64-67,共4页
Automation Panorama1
关键词
相关因子
缺失数据
时间序列法
Correlation factor
Missing data
Time series method
