TY - JOUR
T1 - A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base
AU - Li, Gailing
AU - Zhou, Zhijie
AU - Hu, Changhua
AU - Chang, Leilei
AU - Zhou, Zhiguo
AU - Zhao, Fujun
N1 - Publisher Copyright:
© 2016 Elsevier Ltd
PY - 2017/3/1
Y1 - 2017/3/1
N2 - The safety assessment of complex system is important for implementing and fulfilling the policy of “safety first and prevention oriented”. Most available approaches cannot combine historical data with expert knowledge or cannot handle vague and uncertain information efficiently. In this paper, a new safety assessment model for complex system based on the conditional generalized minimum variance (CGMV) and the belief rule base (BRB) is proposed. In the proposed model, to decrease the computation and improve the accuracy, the conditional generalized minimum variance is used to select the key features. Meanwhile, BRB is utilized to deal with both quantitative and qualitative information under uncertainty. Moreover, to improve the precision and efficiency of BRB, the referenced values for the antecedent attributes are optimized by the fuzzy subtractive clustering algorithm. Meanwhile, the belief degrees are calculated by the modified fuzzy c-means clustering. What's more, the differential evolution (DE) algorithm is used to identify the optimal BRB parameters. The new proposed model is applied to an actual engineering system, which is used to testify the validity of the new model. Compared with other approaches, the proposed model has shown superior accuracy and less computation complexity.
AB - The safety assessment of complex system is important for implementing and fulfilling the policy of “safety first and prevention oriented”. Most available approaches cannot combine historical data with expert knowledge or cannot handle vague and uncertain information efficiently. In this paper, a new safety assessment model for complex system based on the conditional generalized minimum variance (CGMV) and the belief rule base (BRB) is proposed. In the proposed model, to decrease the computation and improve the accuracy, the conditional generalized minimum variance is used to select the key features. Meanwhile, BRB is utilized to deal with both quantitative and qualitative information under uncertainty. Moreover, to improve the precision and efficiency of BRB, the referenced values for the antecedent attributes are optimized by the fuzzy subtractive clustering algorithm. Meanwhile, the belief degrees are calculated by the modified fuzzy c-means clustering. What's more, the differential evolution (DE) algorithm is used to identify the optimal BRB parameters. The new proposed model is applied to an actual engineering system, which is used to testify the validity of the new model. Compared with other approaches, the proposed model has shown superior accuracy and less computation complexity.
KW - Belief rule base
KW - Conditional generalized minimum variance method
KW - Safety assessment
UR - http://www.scopus.com/inward/record.url?scp=85000843766&partnerID=8YFLogxK
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U2 - 10.1016/j.ssci.2016.11.011
DO - 10.1016/j.ssci.2016.11.011
M3 - Article
AN - SCOPUS:85000843766
SN - 0925-7535
VL - 93
SP - 108
EP - 120
JO - Safety Science
JF - Safety Science
ER -