Self-tuning of fuzzy belief rule bases for engineering system safety analysis
Annals of Operations ResearchPublished 10 March 2008
Jun Liu, Jian-Bo Yang, Da Ruan, Luis Martı́nez, Jin Wang
Citations79
SJR quartileQ1
SJR score1.09
SNIP1.62
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TL;DR
The methods for self-tuning a FBRB for engineering system safety analysis are investigated on the basis of a previous study and a case study is used to demonstrate how the methods can be implemented.
Abstract
A framework for modelling the safety of an engineering system using a fuzzy\nrule-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\ninvestigated 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.
Keywords
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