Please use this identifier to cite or link to this item: https://doi.org/10.1109/IEEM.2010.5674279
Title: Application of stochastic approximation methods for stochastic computer models calibration
Authors: Yuan, J.
Ng, S.H. 
Tsui, K.L.
Keywords: Finite difference SA
Microsimulation model
Simultaneous perturbation SA
Stochastic approximation
Stochastic computer model calibration
Issue Date: 2010
Source: Yuan, J.,Ng, S.H.,Tsui, K.L. (2010). Application of stochastic approximation methods for stochastic computer models calibration. IEEM2010 - IEEE International Conference on Industrial Engineering and Engineering Management : 1606-1610. ScholarBank@NUS Repository. https://doi.org/10.1109/IEEM.2010.5674279
Abstract: Computer models are widely used to simulate real processes. Within the computer model, there always exist some parameters which are unobservable in the real process but need to be specified in the model. The procedure to adjust these unknown parameters in order to fit the model to observed data and improve its predictive capability is known as calibration. In this paper, we propose an effective and efficient algorithm based on the stochastic approximation approach for stochastic computer model calibration. We first demonstrate the feasibility of applying stochastic approximation to calibration and apply it to two stochastic simulation models. We compare our proposed SA approach to another direct calibration search method, the genetic algorithm. The results indicate that our proposed SA approach performs equally as well in terms of accuracy and significantly better in terms of computational search time. ©2010 IEEE.
Source Title: IEEM2010 - IEEE International Conference on Industrial Engineering and Engineering Management
URI: http://scholarbank.nus.edu.sg/handle/10635/72287
ISBN: 9781424485031
DOI: 10.1109/IEEM.2010.5674279
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

2
checked on Dec 11, 2017

Page view(s)

31
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.