.png){kind=icon}
返回
Regularized error-in-variable estimation for big data modeling and process analytics
.png){kind=icon}
PRE
AI
DOI：10.1016/j.conengprac.2021.105060.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article addresses estimating the uncertainty in operational data by introducing a regularized modeling technique. Existing work (i) requires knowing the true dimension of the operational data, (ii) relies on a maximum likelihood estimation that is compromised by a stringent restriction for this true dimension and (iii) is computationally expensive. In contrast, the presented regularized error-in-variable technique (i) allows determining the true data dimension through hypothesis testing, (ii) is not limited by the restriction of existing methods, and (iii) has an objective function that can be solved efficiently. Based on a simulation example and the analysis of two industrial datasets, the paper highlights that the regularized estimation technique outperforms existing work and shows how to embed this technique within an advanced process analytics framework for advanced process control, optimization and general process diagnostics.
Keyword:
Error-in-variable models
Regularization
Process data analytics
Parameter estimation
Source signal extraction
Big data
期刊
IF:
4.6
论文数:
5.7K
被引数:
1.1W
机构
引用论文
.png){kind=icon}
A novel process monitoring approach based on variational recurrent autoencoder一种基于变分递归自编码器的过程监控新方法
OAAI
OAAI分享收藏
分享收藏
查看更多内容