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

(2008) ANNALS OF OPERATIONS RESEARCH. 163(1). p.143-168
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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.
Keywords
INFERENCE, SETS, UNCERTAINTY, MULTIATTRIBUTE DECISION-ANALYSIS, EVIDENTIAL REASONING APPROACH, optimization, evidential reasoning, belief rule-base, fuzzy logic, uncertainty, safety analysis

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Citation

Please use this url to cite or link to this publication:

MLA
Liu, Jun, Jian-Bo Yang, Da Ruan, et al. “Self-tuning of Fuzzy Belief Rule Bases for Engineering System Safety Analysis.” ANNALS OF OPERATIONS RESEARCH 163.1 (2008): 143–168. Print.
APA
Liu, Jun, 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.
Chicago author-date
Liu, Jun, Jian-Bo Yang, Da Ruan, Luis Martinez, and Jin Wang. 2008. “Self-tuning of Fuzzy Belief Rule Bases for Engineering System Safety Analysis.” Annals of Operations Research 163 (1): 143–168.
Chicago author-date (all authors)
Liu, Jun, Jian-Bo Yang, Da Ruan, Luis Martinez, and Jin Wang. 2008. “Self-tuning of Fuzzy Belief Rule Bases for Engineering System Safety Analysis.” Annals of Operations Research 163 (1): 143–168.
Vancouver
1.
Liu J, Yang J-B, Ruan D, Martinez L, Wang J. Self-tuning of fuzzy belief rule bases for engineering system safety analysis. ANNALS OF OPERATIONS RESEARCH. 2008;163(1):143–68.
IEEE
[1]
J. Liu, J.-B. Yang, D. Ruan, L. Martinez, and J. Wang, “Self-tuning of fuzzy belief rule bases for engineering system safety analysis,” ANNALS OF OPERATIONS RESEARCH, vol. 163, no. 1, pp. 143–168, 2008.
@article{2918818,
  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.},
  author       = {Liu, Jun and Yang, Jian-Bo and Ruan, Da and Martinez, Luis and Wang, Jin},
  issn         = {0254-5330},
  journal      = {ANNALS OF OPERATIONS RESEARCH},
  keywords     = {INFERENCE,SETS,UNCERTAINTY,MULTIATTRIBUTE DECISION-ANALYSIS,EVIDENTIAL REASONING APPROACH,optimization,evidential reasoning,belief rule-base,fuzzy logic,uncertainty,safety analysis},
  language     = {eng},
  number       = {1},
  pages        = {143--168},
  title        = {Self-tuning of fuzzy belief rule bases for engineering system safety analysis},
  url          = {http://dx.doi.org/10.1007/s10479-008-0327-0},
  volume       = {163},
  year         = {2008},
}

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