Advertisement

Credibility Assessment of Simulation Models Using Hesitant Cloud Linguistic Term Sets

  • Xiaojun YangEmail author
  • Zhongfu Xu
  • Chuan Shi
  • Hao Lei
  • Changwei Yan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

Credibility assessment of a simulation model is a hard problem as subjectivity is always involved. Linguistic expressions are much more straightforward and friendlier for a subject matter expert (SME) to represent qualitative judgments. Therefore, an assessment method based on hesitant cloud linguistic term sets (HCLTSs) is proposed in this paper. SMEs use context-free grammars and linguistic expressions to express their opinions. Then, these linguistic expressions are transformed into HCLTSs. Finally, aggregation results are calculated by synthetic cloud algorithm and weighted average cloud algorithm. An example under multiple-criteria group decision making is presented to illustrate the proposed method for credibility assessment.

Keywords

Credibility assessment Simulation model Hesitant cloud linguistic term sets Cloud model Linguistic expressions 

Notes

Acknowledgment

This work was supported by the Equipment Pre-Research Project of the ‘Thirteenth Five-Year-Plan’ of China under Grant 6140001010506.

References

  1. 1.
    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. IEEE, San Diego (2002)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Balci, O.: How to assess the acceptability and credibility of simulation results. In: Proceedings of the 1989 Winter Simulation Conference, pp. 62–71. ACM, Washington (1989)Google Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Li, D.Y., Meng, H.J., Shi, X.M.: Membership clouds and membership cloud generators. Comput. Res. Dev. 42(8), 32–41 (1995)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Li, D.Y., Liu, C.Y., Gan, W.Y.: A new cognitive model: cloud model. Int. J. Intell. Syst. 24, 357–375 (2009)CrossRefGoogle Scholar
  10. 10.
    Li, D.Y., Du, Y.: Artificial Intelligence with Uncertainty. Chapman & Hall/CRC Press, Boca Raton (2007)CrossRefGoogle Scholar
  11. 11.
    Huang, H.C., Yang, X.J.: Representation of the pairwise comparisons in AHP using hesitant cloud linguistic term sets. Fundam. Inform. 144(3–4), 349–362 (2016)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Rodríguez, R.M., Martínez, L., Herrera, F.: Hesitant fuzzy linguistic term sets for decision making. IEEE Trans. Fuzzy Syst. 20(1), 109–119 (2012)CrossRefGoogle Scholar
  13. 13.
    Sargent, R.G.: Verification and validation of simulation models. J. Simul. 7, 12–24 (2013)CrossRefGoogle Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    Olsen, M.M., Raunak, M., Setteducati, M.: Enabling quantified validation for model credibility. In: Proceedings of the 50th Computer Simulation Conference, pp. 1–10. Society for Computer Simulation International, Bordeaux, France (2018)Google Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Luoyang Electronic Equipment Test Center of ChinaLuoyangChina

Personalised recommendations