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An Evidence-Combination-Based Simulation Result Validation Method for Multi-source Data

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Modeling, Design and Simulation of Systems (AsiaSim 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 751))

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Simulation result validation is a crucial work in simulation credibility assessment. The result validation metric is a measure of agreement between simulation output and experimental observations. Sometimes the observations maybe derive from different data sources such as the actual system output, credible hardware-in-the-loop simulation system output and the expert opinions and so on. Then the agreement analysis results of system response would be multiple certainly. To solve the simulation result validation with multi-source data, the paper proposes a result validation method based on evidence combination. First, the evidence representation methods for multi-source data on the structure of evidence space are proposed. Then the multiple evidence bodies are aggregated based on evidence combination rules and the integrated validation result could be achieved. In the end, the application process and effectiveness of this validation method is illustrated through a numerical example.

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  1. Sargent, R.G.: Verification and validation of simulation models. J. Simul. 7(1), 12–24 (2013)

    Article  MathSciNet  Google Scholar 

  2. Sornette, D., Davis, A.B., Ide, K., Vixie, K.R., Pisarenko, V., Kamm, J.R.: Algorithm for model validation: theory and applications. Proc. Natl. Acad. Sci. U.S.A. 104, 6562–6567 (2007). National Acad Sciences, USA

    Article  Google Scholar 

  3. Jiang, X., Yuan, Y., Mahadevan, S., Liu, X.: An investigation of Bayesian inference approach to model validation with non-normal data. J. Stat. Comput. Simul. 83(10), 1829–1851 (2013)

    Article  MathSciNet  Google Scholar 

  4. Zhao, L., Lu, Z., Yun, W., Wang, W.: Validation metric based on Mahalanobis distance for models with multiple correlated responses. Reliab. Eng. Syst. Saf. 159, 80–89 (2017)

    Article  Google Scholar 

  5. Roy, C.J., Oberkampf, W.L.: A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing. Comput. Methods Appl. Mech. Eng. 200, 2131–2144 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  6. Tien, I., Kiureghian, A.D.: Algorithms for Bayesian network modeling and reliability assessment of infrastructure systems. Reliab. Eng. Syst. Saf. 156, 134–147 (2016)

    Article  Google Scholar 

  7. Zaman, K., McDonald, M., Mahadevan, S.: Inclusion of correlation effects in model prediction under data uncertainty. Probab. Eng. Mech. 34, 58–66 (2013)

    Article  Google Scholar 

  8. Zhan, Z., Fu, Y., Yang, R.J.: Bayesian based multivariate model validation method under uncertainty for dynamic systems. J. Mech. Des. 134, 034502(1–7) (2012)

    Article  Google Scholar 

  9. Jiang, X., Mahadevan, S., Urbina, A.: Bayesian nonlinear structural equation modeling for hierarchical validation of dynamical systems. Mech. Syst. Signal Process. 24, 957–975 (2010)

    Article  Google Scholar 

  10. Huang, T.C.K., Chen, Y.L., Chang, T.H.: A novel summarization technique for the support of resolving multi-criteria decision making problems. Decis. Support Syst. 79, 109–124 (2015)

    Article  Google Scholar 

  11. Dempster, A.: Upper and lower probabilities induced by multivalued mapping. Ann. Math. Stat. 38, 325–339 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  12. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  13. Sentz, K., Ferson, S.: Combination of evidence in dempster-shafer theory. Technical report, Sandia National Laboratories (2002)

    Google Scholar 

  14. Jousselme, A.L., Grenier, D., Bosse, E.: A new distance between two bodies of evidence. Inf. Fusion 2, 91–101 (2001)

    Article  Google Scholar 

  15. Serfling, R.J.: Approximation Theorems of Mathematical Statistics, pp. 138–170. Wiley, Hoboken (1980)

    MATH  Google Scholar 

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This research is supported by the National Natural Science Foundation of China (Grant No. 61403097).

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Correspondence to Ping Ma .

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Lin, S., Li, W., Ma, P., Yang, M., Huo, J. (2017). An Evidence-Combination-Based Simulation Result Validation Method for Multi-source Data. In: Mohamed Ali, M., Wahid, H., Mohd Subha, N., Sahlan, S., Md. Yunus, M., Wahap, A. (eds) Modeling, Design and Simulation of Systems. AsiaSim 2017. Communications in Computer and Information Science, vol 751. Springer, Singapore.

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  • Print ISBN: 978-981-10-6462-3

  • Online ISBN: 978-981-10-6463-0

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