Journal ArticleDOI
Metamodels for variable importance decomposition with applications to probabilistic engineering design
TLDR
This paper aims at contributing to reduce the limitation of studies that simultaneously consider the combination of metamodeling and sensitivity analysis and the environments in which they operate the best by basing the study on multiple metrics and using two test problems.About:
This article is published in Computers & Industrial Engineering.The article was published on 2009-10-01. It has received 28 citations till now. The article focuses on the topics: Metamodeling & Sensitivity (control systems).read more
Citations
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Journal ArticleDOI
Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications
TL;DR: A comprehensive review of global SA methods in the field of hydrological modeling, including the relationship between parameter identification, uncertainty analysis, and optimization in hydrology, and how to deal with correlated parameters, and time-varying SA is provided.
Book
Taguchi on robust technology development : bringing quality engineering upstream
TL;DR: Quality and productivity methods for evaluating quality parameter design methods for specifying tolerance quality management for production processes as discussed by the authors, for example, have been used to evaluate quality parameters in the context of production processes.
Journal ArticleDOI
Computing derivative-based global sensitivity measures using polynomial chaos expansions
Bruno Sudret,Chu V. Mai +1 more
TL;DR: In this paper, the authors show how polynomial chaos expansions may be used to compute analytically DGSMs as a mere post-processing, which requires the analytical derivation of derivatives of the orthonormal polynomials which enter PC expansions.
Journal ArticleDOI
Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach
TL;DR: A two-step statistical evaluation framework based on a screening method for qualitative ranking of parameters and a variance-based method integrated with a meta-model for quantitative sensitivity analysis that can not only quantify the sensitivity, but also reduce the computational cost with good accuracy compared to the classical approaches.
Journal ArticleDOI
Improved Similarity Measure in Case-Based Reasoning with Global Sensitivity Analysis: An Example of Construction Quantity Estimating
Jing Du,Jeff Bormann +1 more
TL;DR: It is found that when measuring the similarity between the new project and historical projects, traditional similarity measure methods fail to consider the nonlinearity and muticollinearity embedded in the problem, as well as differences across crafts.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Journal ArticleDOI
A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
TL;DR: In this paper, two sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies and they are shown to be improvements over simple sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.