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Engineering Optimization: Applications, Methods, and Analysis
ISBN:
9781118936337
No. of Pages:
770
Publisher:
ASME Press
Publication date:
2018
This chapter is a summary of key issues and solutions related to nonlinear regression–fitting nonlinear models to data. The details of diverse issues are revealed in the book Nonlinear Regression Modeling for Engineering Applications: Modeling, Model Validation, and Enabling Design of Experiments by Rhinehart, R. R., John Wiley & Sons, Inc., Hoboken, NJ, 2016b. Here, they are summarized and presented with an optimization application perspective.
29.1
Introduction
29.2Perspective
29.3Least Squares Regression: Traditional View on Linear Model Parameters
29.4Models Nonlinear in DV
29.5Maximum Likelihood
29.6Convergence Criterion
29.7Model Order or Complexity
29.8Bootstrapping to Reveal Model Uncertainty
29.9Perspective
29.10Takeaway
29.11Exercises
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