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Metamodeling complex systems using linear and nonlinear regression methods
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Date
2007
Author
Kartal, Elçin
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Metamodeling is a very popular approach for the approximation of complex systems. Metamodeling techniques can be categorized according to the type of regression method employed as linear and nonlinear models. The Response Surface Methodology (RSM) is an example of linear regression. In classical RSM metamodels, parameters are estimated using the Least Squares (LS) Method. Robust regression techniques, such as Least Absolute Deviation (LAD) and M-regression, are also considered in this study due to the outliers existing in data sets. Artificial Neural Networks (ANN) and Multivariate Adaptive Regression Splines (MARS) are examples for non-linear regression technique. In this thesis these two nonlinear metamodeling techniques are constructed and their performances are compared with the performances of linear models.
Subject Keywords
Statistics.
,
General Science.
URI
http://etd.lib.metu.edu.tr/upload/2/12608930/index.pdf
https://hdl.handle.net/11511/16827
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Graduate School of Natural and Applied Sciences, Thesis
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E. Kartal, “Metamodeling complex systems using linear and nonlinear regression methods,” M.S. - Master of Science, Middle East Technical University, 2007.
