.png){kind=icon}
返回
Techniques for solving industrial nonlinear data reconciliation problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compchemeng.2004.06.009.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The focus of this short note is to highlight several techniques to solve industrial nonlinear data reconciliation problems. The main areas of discussion are starting value generation, row and column scaling, regularization of the kernel matrix, using different and independent unconstrained solving methods such as ridge regression, matrix projection, Newton's method and singular value decomposition and infeasibility handling. These techniques are usually necessary to arrive at solutions to nonlinear reconciliation problems which are poorly initiated, ill-conditioned and even inconsistent. A relatively large and well-studied numerical example is solved taken from the mining process industry which demonstrates some of the techniques discussed. (C) 2004 Elsevier Ltd. All rights reserved.
Keyword:
starting values
scaling
ridge regression
infeasibilities
regularization
gradient projection
Newton methods
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
3.9
论文数:
8.1K
被引数:
1.7W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
查看更多内容