Abstract
Comprehensive and objective credibility assessments of complex simulation models are crucial to the successful application of models and simulation results in various critical evaluation and decision making problems. However, the credibility assessment of a complex simulation model usually encounters many challenges, involves the measurements and evaluations of hundreds of qualitative and quantitative indicators, and requires the integration of heterogeneous results. Therefore, cloud models which can describe both fuzziness and randomness are adopted to represent and aggregate diverse evaluation results of various qualitative and quantitative indicators. Then, crisp values, interval numbers, statistical data and linguistic terms can all be represented and aggregated by normal cloud models. The main advantages of our methods are that diverse evaluation results of various indicators can be represented and aggregated, and uncertainties associated with these results of leaf indicators can be preserved and propagated into the final assessment result. A missile simulation model credibility assessment example is presented to illustrate the proposed methods.
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References
Balci, O., Adams, R.J., Myers, D.S., Nance, R.E.: A collaborative evaluation environment for credibility assessment of modeling and simulation applications. In: Proceedings of the 2002 Winter Simulation Conference, pp. 214–220 (2002)
Yang, Y.N., Kumaraswamy, M.M., Pam, H.J., Mahesh, G.: Integrated qualitative and quantitative methodology to assess validity and credibility of models for bridge maintenance management system development. J. Manag. Eng. 27(3), 149–158 (2011)
Liao, W.C., Zhang, J., Zheng, X.P., Zhao, Y.: A generalized validation procedure for pedestrian models. Simul. Model. Pract. Theory 77, 20–31 (2017)
Olsen, M.M., Raunak, M., Setteducati, M.: Enabling quantified validation for model credibility. In: Proceedings of the 50th Computer Simulation Conference, pp. 1–10 (2018)
Balci, O.: A methodology for certification of modeling and simulation applications. ACM Trans. Model. Comput. Simul. 11(4), 352–377 (2001)
Azadeh, A., Abdolhossein Zadeh, S.: An integrated fuzzy analytic hierarchy process and fuzzy multiple-criteria decision-making simulation approach for maintenance policy selection. Simulation 92(1), 3–18 (2016)
Wu, D.R., Mendel, J.M.: Computing with words for hierarchical decision making applied to evaluating a weapon system. IEEE Trans. Fuzzy Syst. 18(3), 441–460 (2010)
Li, D.Y., Meng, H.J., Shi, X.M.: Membership clouds and membership cloud generators. Comput. Res. Dev. 42(8), 32–41 (1995)
Li, D.Y., Han, J.W., Shi, X.M., Chan, M.C.: Knowledge representation and discovery based on linguistic atoms. Knowl.-Based Syst. 10(7), 431–440 (1998)
Yang, X.J., Yan, L.L., Zeng, L.: How to handle uncertainties in AHP: the cloud Delphi hierarchical analysis. Inf. Sci. 222, 384–404 (2013)
Li, D.Y., Liu, C.Y., Gan, W.Y.: A new cognitive model: cloud model. Int. J. Intell. Syst. 24, 357–375 (2009)
Li, D.Y., Du, Y.: Artificial Intelligence with Uncertainty. Chapman & Hall/CRC Press, Boca Raton (2007)
Yang, X.J., Yan, L.L., Peng, H., Gao, X.D.: Encoding words into cloud models from interval-valued data via fuzzy statistics and membership function fitting. Knowl.-Based Syst. 55, 114–124 (2014)
Liu, C.Y., Feng, M., Dai, X.J., et al.: A new algorithm of backward cloud. J. Syst. Simul. 16(11), 2417–2420 (2004)
Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 81–97 (1956)
Yang, X.J., Zeng, L., Zhang, R.: Cloud Delphi method. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 20(1), 77–97 (2012)
Kheir, N.A., Holmes, W.M.: On validating simulation models of missile systems. Simulation 30(4), 117–128 (1978)
Yang, X.J., Xu, Z.F., Ouyang, H.B., Zhang, X.: Experimental comparison of some classical distance measures for time series data in simulation model validation. In: Proceedings of the 2019 IEEE 8th Data Driven Control and Learning Systems Conference (2019, accepted)
Yang, X.J., Xu, Z.F., Ouyang, H.B., Wang, L.H.: Credibility assessment of simulation models using flexible mapping functions. In: Proceedings of the 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (2019, accepted)
Acknowledgment
This work was supported by the Equipment Pre-Research Project of the ‘Thirteenth Five-Year-Plan’ of China under Grant 6140001010506.
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Yang, X., Xu, Z., He, R., Xue, F. (2019). Credibility Assessment of Complex Simulation Models Using Cloud Models to Represent and Aggregate Diverse Evaluation Results. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_28
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DOI: https://doi.org/10.1007/978-3-030-26766-7_28
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