.png){kind=icon}
返回
Parameter set selection for estimation of nonlinear dynamic systems
.png){kind=icon}
PRE
AI
DOI：10.1002/aic.11295.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A new approach is introduced for parameter set selection for nonlinear systems that takes nonlinearity of the parameter-output sensitivity, the effect that uncertainties in the nominal values of the parameters have and the effect that inputs and initial conditions have on parameter selection into account. In a first step, a collection of (sub)optimal parameter sets is determined for the nominal values of the parameters using a genetic algorithm. These parameter sets are then further analyzed for uncertainty in the parameters and changes in the initial conditions and inputs using differential analysis as well as a sampling-based approach to determine the key factors influencing sensitivity and the likelihood of a parameter set to be the optimal set under these varying conditions. The outcome of this procedure is a collection of parameter sets, which can be used for parameter estimation and additional information about how likely it is that a set is optimal for parameter estimation. Additionally, the size of the region in parameter space in which a certain set of parameters will remain optimal is determined. (C) 2007 American Institute of Chemical Engineers.
Keyword:
parameter selection
parameter estimation
experimental design
sensitivity analysis
nonlinear dynamic systems
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
IF:
4
论文数:
1.1W
被引数:
2.9W
机构
暂无机构信息
引用论文
分享收藏Practical identifiability of ASM2d parameters -: systematic selection and tuning of parameter subsets.png)
WATER RESEARCH
WATER RESEARCHIF12.4
PREAI
分享收藏
查看更多内容