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Self-tuning of fuzzy belief rule bases for engineering system safety analysis

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  • Jun Liu
  • Jian-Bo Yang
  • Da Ruan
  • Luis Martinez
  • Jin Wang

Abstract

A framework for modelling the safety of an engineering system using a fuzzy rule-based evidential reasoning (FURBER) approach has been recently proposed, where a fuzzy rule-base designed on the basis of a belief structure (called a belief rule base) forms a basis in the inference mechanism of FURBER. However, it is difficult to accurately determine the parameters of a fuzzy belief rule base (FBRB) entirely subjectively, in particular for complex systems. As such, there is a need to develop a supporting mechanism that can be used to train in a locally optimal way a FBRB initially built using expert knowledge. In this paper, the methods for self-tuning a FBRB for engineering system safety analysis are investigated on the basis of a previous study. The method consists of a number of single and multiple objective nonlinear optimization models. The above framework is applied to model the system safety of a marine engineering system and the case study is used to demonstrate how the methods can be implemented. Copyright Springer Science+Business Media, LLC 2008

Suggested Citation

  • Jun Liu & Jian-Bo Yang & Da Ruan & Luis Martinez & Jin Wang, 2008. "Self-tuning of fuzzy belief rule bases for engineering system safety analysis," Annals of Operations Research, Springer, vol. 163(1), pages 143-168, October.
    • Handle: RePEc:spr:annopr:v:163:y:2008:i:1:p:143-168:10.1007/s10479-008-0327-0
    DOI: 10.1007/s10479-008-0327-0
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    References listed on IDEAS

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    1. Wang, Ying-Ming & Yang, Jian-Bo & Xu, Dong-Ling, 2006. "Environmental impact assessment using the evidential reasoning approach," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1885-1913, November.
    2. Yang, Jian-Bo, 2001. "Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties," European Journal of Operational Research, Elsevier, vol. 131(1), pages 31-61, May.
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    Cited by:

    1. Alberto Calzada & Jun Liu & Hui Wang & Anil Kashyap, 2012. "An empirical comparative study for urban regeneration: measuring the effectiveness of DSS and GIS approaches," ERES eres2012_148, European Real Estate Society (ERES).
    2. Xinyang Deng & Yong Deng & Felix Chan, 2014. "An improved operator of combination with adapted conflict," Annals of Operations Research, Springer, vol. 223(1), pages 451-459, December.
    3. Dong-Ling Xu, 2012. "An introduction and survey of the evidential reasoning approach for multiple criteria decision analysis," Annals of Operations Research, Springer, vol. 195(1), pages 163-187, May.
    4. Hua Zhu & Jianbin Zhao & Yang Xu & Limin Du, 2016. "Interval-Valued Belief Rule Inference Methodology Based on Evidential Reasoning-IRIMER," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1345-1366, November.
    5. Wan, Chengpeng & Yan, Xinping & Zhang, Di & Yang, Zaili, 2019. "A novel policy making aid model for the development of LNG fuelled ships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 29-44.
    6. Jun Liu & Luis Martinez & Da Ruan & Rosa Rodriguez & Alberto Calzada, 2011. "Optimization algorithm for learning consistent belief rule-base from examples," Journal of Global Optimization, Springer, vol. 51(2), pages 255-270, October.

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