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Self-tuning of fuzzy belief rule bases for engineering system safety analysis
Liu, J.; Yang, J.-B.; Ruan, D.; Martinez, L.; Wang, J. (2008). Self-tuning of fuzzy belief rule bases for engineering system safety analysis. Annals of Operations Research 163(1): 143-168. https://dx.doi.org/10.1007/s10479-008-0327-0
In: Annals of Operations Research. Springer: Dordrecht. ISSN 0254-5330; e-ISSN 1572-9338, meer
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safety analysis; uncertainty; fuzzy logic; belief rule-base; evidentialreasoning; optimization |
| Auteurs | | Top |
- Liu, J.
- Yang, J.-B.
- Ruan, D., meer
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| 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. |
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