Calibration of stochastic computer models using stochastic approximation methods

Jun Yuan, Szu Hui Ng, Kwok Leung Tsui

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

25 Citations (Scopus)

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 predictive capability is known as calibration. Practically, calibration is typically done manually. In this paper, we propose an effective and efficient algorithm based on the stochastic approximation (SA) approach that can be easily automated. We first demonstrate the feasibility of applying stochastic approximation to stochastic computer model calibration and apply it to three stochastic simulation models. We compare our proposed SA approach with 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. We further consider the calibration parameter uncertainty in the subsequent application of the calibrated model and propose an approach to quantify it using asymptotic approximations. © 2004-2012 IEEE.
Original languageEnglish
Article number6213574
Pages (from-to)171-186
JournalIEEE Transactions on Automation Science and Engineering
Volume10
Issue number1
DOIs
Publication statusPublished - 2013
Externally publishedYes

Research Keywords

  • Computer model calibration
  • parameter uncertainty
  • stochastic approximation
  • stochastic computer simulation

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