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Nonlinear Regression Modeling for Engineering Applications: Modeling, Model Validation, and Enabling Design of Experiments

By
R. Russell Rhinehart
R. Russell Rhinehart
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ISBN:
9781118597965
No. of Pages:
400
Publisher:
ASME-Wiley
Publication date:
2016

Optimizers iteratively move the trial solution toward the optimum. It is likely that no iteration will jump exactly on the optimum, but, hopefully, each iteration gets closer to the optimum. Once close enough to the optimum there is no sense in trying to get closer. The optimizer should stop when the transient state (TS) is close enough to the optimum. Seeking infinite digit perfection is usually not justified.

12.1
Introduction
12.2
Convergence versus Stopping
12.3
Traditional Criteria for Claiming Convergence
12.4
Combining DV Influence on OF
12.5
Use Relative Impact as Convergence Criterion
12.6
Steady-State Convergence Criterion
12.7
Neural Network Validation
12.8
Takeaway
Exercises
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