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To get a better understanding on the output uncertainty contributed by an individual variable as well as the correlated variables of models with dependent inputs , a method for decomposing Sobol’s ...
input ratings, while the methods based on RMi produced the lowest results. Sensitivity analyses performed on the simulation outputs show that, for the examined case study, the input parameters reflecting ...
The global sensitivity analysis method used to quantify the influence of uncertain input variables on the variability in numerical model responses has already been applied to deterministic computer ...
function (PDF) of model output and the joint PDF of model output and the input variable by some certain smart techniques. Firstly, a method based on the concepts of maximum entropy, fractional moment ...
In this article, principal component analysis method , which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable ...
is an extension of the popularly used moment-independent sensitivity analysis index. Although it considers the whole probability distribution of dynamic output , it is difficult to estimate the joint cumulative ...
power, duty factor, frequency, and focal positions were used as input process variables , while microhardness and bead area were considered as output variables . Regression analysis was executed so ...
This article develops a new method of estimating inefficiencies in joint production and shows that unlike the approaches utilized in the previous studies of inefficiency, this method maintains ...
Abstract We focus on analysing cost minimising production behaviour in multi‐ output settings. We distinguish between two approaches for modelling the use of joint inputs . The cooperative approach ...
gradients are derived for five distribution types of unknown input random variables to be estimated : normal, lognormal, Gumbel, extreme, and uniform distributions. For derivation of the analytical gradients ...
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