Abstract
When simulation factors are numerous while real-world observed data are sparse, the issue of validating the simulation models is problematic. An extreme case is focused that limited real-world observations are available cross the factor space, and only a single replicate is available on per simulation factor setting. A method based on design of experiments is proposed by which the validation experiments could be well arranged across the factor space through optimal design. The p-value test technology is employed to evaluate the statistical consistency of the static data, and for a set of validation experiments obtained by DoE, the combined analysis of all the p-values resulted from these experiments can be taken based on the inverse-CDF theorem, to make an overall characterization of degree of the simulation credibility on the entire factor space. An example of validation of a guided missile simulation is taken to demonstrate that the method is useful.
Supported by the Fundamental Research Funds of Beihang University (No.YWF-12-LZGF-061).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Law, A.M., Kelton, W.D.: Simulation Modeling and Analysis. Journal of Economic Entomology 82, 1161–1168 (1991)
Liao, Y., Liang, J., Yang, X., Deng, F.: Research on Validation Methods of Six-Degree-of-Freedom Simulation Model of Missile Weapon System. Aerospace Shanghai 6, 34–38 (2007)
Gafarian, A.V., Walsh, J.E.: Statistical Method for Validating Simulation Models by Comparison with Operational Systems. In: Proceeding of the 4th International Conference on Operations Research, New York (1969)
Seila, A.F.: Output Analysis for Simulation. In: Proceedings of the 1990 Winter Simulation Conference, Detroit, Michigan (1990)
Tang, B.: Selecting Latin Hypercubes Using Correlation Criteria. Statistica Sinica 8, 965–978 (1998)
Jin, R., Chen, W., Sudjianto, A.: An Efficient Algorithm for Constructing Optimal Design of Computer Experiments. J. Statist. Plann. Inference 134, 268–287 (2005)
Li, X., Zhang, H.: Improvement of Simulated Annealing Algorithm. Software Guide 7, 47–48 (2008)
Leon-Garcia, A.: Probability and Random Processes for Electrical Engineering, 2nd edn. Addison Wesley Longman (1994)
Smith, B.D.: Simulation Validation Using a Non-Parametric Statistical Method. In: 2006 ITEA Modeling and Simulation Conference, Las Cruces, NM (2006)
Massey, F.J.: The Kolmogorov-Smirnov Test for Goodness of Fit. Journal of the American Statistical Association 46, 68–78 (1951)
Peng, J.: Research on the VV&A Theory and Method of Missile Guidance Simulation System. A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering in Control Science and Engineering, Graduate School of National University of Defense Technology, Changsha, Hunan (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dong, D., Wang, J., Zhang, P. (2012). A Simulation Model Validation Method Based on Design of Experiments. In: Xiao, T., Zhang, L., Fei, M. (eds) AsiaSim 2012. AsiaSim 2012. Communications in Computer and Information Science, vol 323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34384-1_52
Download citation
DOI: https://doi.org/10.1007/978-3-642-34384-1_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34383-4
Online ISBN: 978-3-642-34384-1
eBook Packages: Computer ScienceComputer Science (R0)