Annals of Operations Research

, Volume 163, Issue 1, pp 143-168

First online:

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

  • Jun LiuAffiliated withSchool of Computing and Mathematics, Faculty of Computing and Engineering, University of Ulster at Jordanstown Email author 
  • , Jian-Bo YangAffiliated withManchester Business School (East), The University of Manchester
  • , Da RuanAffiliated withBelgian Nuclear Research Centre (SCK•CEN)
  • , Luis MartinezAffiliated withDepartment of Computer Science, University of Jaén
  • , Jin WangAffiliated withSchool of Engineering, Liverpool John Moores University

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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