Annals of Operations Research

, Volume 163, Issue 1, pp 143–168

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


    • School of Computing and Mathematics, Faculty of Computing and EngineeringUniversity of Ulster at Jordanstown
  • Jian-Bo Yang
    • Manchester Business School (East)The University of Manchester
  • Da Ruan
    • Belgian Nuclear Research Centre (SCK•CEN)
  • Luis Martinez
    • Department of Computer ScienceUniversity of Jaén
  • Jin Wang
    • School of EngineeringLiverpool John Moores University

DOI: 10.1007/s10479-008-0327-0

Cite this article as:
Liu, J., Yang, J., 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 analysisUncertaintyFuzzy logicBelief rule-baseEvidential reasoningOptimization

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© Springer Science+Business Media, LLC 2008