.png){kind=icon}
Return
Analyzing sensitivity measures using moment-matching technique
.png){kind=icon}
OA
AI
DOI：10.1016/j.ress.2016.10.020.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Sensitivity indices are used to rank the importance of input design variables or components by estimating the degree of uncertainty of output variable influenced by the uncertainty generated from input variables or components. With the advent of highly complex engineering simulation models that describe the relationship between input variables and output response, the need for an efficient and effective sensitivity analysis is more demanding. Traditional importance measures either requires extensive random number generations or unable to measure variables interaction effects. In this article, a generalized approach that can provide efficient and accurate global sensitivity indices is developed. The approach consists of two steps; running an orthogonal array based experiment using moment-matched levels of the input variables followed by a variance contribution analysis. The benefits of the approach are demonstrated through different real life examples.
Keywords:
Sensitivity index
Moment matching
Probabilistic output
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
R
IF:
11
Papers:
9.0K
Citations:
4.2W
Organization
Cited Papers
ShareSaveMoral and Ethical Antecedents of Attitude Toward Counterfeit Luxury Products: Evidence from Pakistan
PREAI
ShareSaveShareSaveShareSaveShareSaveA ROBUST MEASURE OF UNCERTAINTY IMPORTANCE FOR USE IN FAULT TREE SYSTEM-ANALYSIS.png)
RISK ANALYSIS
RISK ANALYSISIF3.3
PREAI
ShareSaveShareSaveShareSave
View more