Annals of Operations Research

, Volume 163, Issue 1, pp 143–168

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

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

DOI: 10.1007/s10479-008-0327-0

Cite this article as:
Liu, J., Yang, JB., Ruan, D. et al. Ann Oper Res (2008) 163: 143. doi:10.1007/s10479-008-0327-0


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.


Safety analysis Uncertainty Fuzzy logic Belief rule-base Evidential reasoning Optimization 

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Jun Liu
    • 1
  • Jian-Bo Yang
    • 2
  • Da Ruan
    • 3
  • Luis Martinez
    • 4
  • Jin Wang
    • 5
  1. 1.School of Computing and Mathematics, Faculty of Computing and EngineeringUniversity of Ulster at JordanstownNewtownabbeyUK
  2. 2.Manchester Business School (East)The University of ManchesterManchesterUK
  3. 3.Belgian Nuclear Research Centre (SCK•CEN)MolBelgium
  4. 4.Department of Computer ScienceUniversity of JaénJaénSpain
  5. 5.School of EngineeringLiverpool John Moores UniversityLiverpoolUK

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