Name
Title | ||
|---|---|---|
Calibrating simulation models using the knowledge gradient with continuous parameters |
Abstract | ||
|---|---|---|
We describe an adaptation of the knowledge gradient, originally developed for discrete ranking and selection problems, to the problem of calibrating continuous parameters for the purpose of tuning a simulator. The knowledge gradient for continuous parameters uses a continuous approximation of the expected value of a single measurement to guide the choice of where to collect information next. We show how to find the parameter setting that maximizes the expected value of a measurement by optimizing a continuous but nonconcave surface. We compare the method to sequential kriging for a series of test surfaces, and then demonstrate its performance in the calibration of an expensive industrial simulator. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/WSC.2010.5679082 | Winter Simulation Conference |
Keywords | Field | DocType |
calibration,simulation,statistical analysis,continuous parameters,discrete ranking,industrial simulator,knowledge gradient,sequential kriging,simulation model calibration,simulator tuning | Computer science,Simulation modeling,Artificial intelligence,Gaussian process,Kriging,Mathematical optimization,Ranking,Simulation,Continuous approximation,Expected value,Covariance matrix,Calibration,Machine learning | Conference |
ISSN | ISBN | Citations |
0891-7736 | 978-1-4244-9866-6 | 3 |
PageRank | References | Authors |
0.47 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Warren R. Scott | 1 | 3 | 0.47 |
| Warren B. Powell | 2 | 1614 | 151.46 |
| Hugo Simão | 3 | 106 | 8.38 |