Multivariate Validation Method Under Correlation for Simulation Model

被引：0
|
作者：
Lin S.-L. [1 ]
Li W. [1 ]
Yang M. [1 ]
Ma P. [1 ]
机构：
[1] Control and Simulation Center, Harbin Institute of Technology, Harbin
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2019年 / 45卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Area metric; Multivariate output; Simulation model validation; Variable correlation; Variable selection;
D O I：
10.16383/j.aas.c180456
中图分类号：
学科分类号：
摘要：
Complex simulation models often generate the multivariate and different types of output, some problems such as the lacking variables information and the inaccuracy correlation measurement are involved in the existing validation methods. A novel validation method combining variables selection with area metric is proposed, the multiple outputs with correlation are selected for the associated validation under uncertainty. The fractal dimension and mutual information methods are primarily applied to analyze the correlation among multivariate and divise responses and extract the correlated variable subsets. Next, the interesting data characteristics of all variables are extracted in subset and the corresponding joint cumulative distribution function (JCDF) of each subset related to any characteristic is calculated. The area metric is used to measure the difference between the simulation and reference output JCDFs of multivariate characteristics in each subset, and the differences are transformed into the consistency degrees. Then the multiple validation results are integrated to obtain the model credibility. Finally, the method is validated through the application case and comparison experiments. Copyright © 2019 Acta Automatica Sinica. All rights reserved.
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收藏
页码：1666 / 1678
页数：12
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共 30 条
  • [1] Li W., Jiao S., Lu L.-Y., Yang M., Validation and selection of simulation model based on the feature differences, Acta Automatica Sinica, 40, 10, pp. 2134-2144, (2014)
  • [2] Yang M., Qian X.-C., Li W., Simulation dynamic output validation method based on the data feature, Systems Engineering and Electronics, 38, 2, pp. 457-463, (2016)
  • [3] Oberkampf W.L., Trucano T.G., Hirsch C., Verification, validation, and predictive capability in computational engineering and physics, Applied Mechanics Reviews, 57, 5, pp. 345-384, (2004)
  • [4] Oberkampf W.L., Barone M.F., Measures of agreement between computation and experiment: Validation metrics, Journal of Computational Physics, 217, 1, pp. 5-36, (2006)
  • [5] Oberkampf W.L., Trucano T.G., Verification and validation in computational fluid dynamics, Progress in Aerospace Sciences, 38, 1, pp. 209-272, (2002)
  • [6] Liu Y., Chen W., Arendt P., Huang H.Z., Towards a better understanding of model validation metrics, Journal of Mechanical Design, 133, 7, (2011)
  • [7] Ling Y., Mahadevan S., Quantitative model validation techniques: New insights, Reliability Engineering and System Safety, 111, pp. 217-231, (2013)
  • [8] Rebba R., Huang S.P., Liu Y.M., Mahadevan S., Statistical validation of simulation models, International Journal of Materials and Product Technology, 25, 1-3, pp. 164-181, (2006)
  • [9] Jiang X.M., Yuan Y., Mahadevan S., Liu X., An investigation of Bayesian inference approach to model validation with non-normal data, Journal of Statistical Computation and Simulation, 83, 10, pp. 1829-1851, (2013)
  • [10] Sankararaman S., Mahadevan S., Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems, Reliability Engineering & System Safety, 138, pp. 194-209, (2015)
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