Indirect disjunctive belief rule base modeling using limited conjunctive rules: Two possible means

Chang, Leilei; Chen, Yu‐Wang; Hao, Zhiyong; Zhou, Zhijie; Xu, Xiaobin; Tan, Xu

doi:10.1016/j.ijar.2019.02.006

Cited by 16 publications

(9 citation statements)

References 31 publications

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“…According to the formula (27), the performance of the proposed approach on the mapping relationship between the input data x i and the output data y needs to be verified where the value range of the independent variable x i (i = 1, 2, 3, 4) is [0.17, 0.88], [2,4], [1,7], and [0, 2]. According to the range of x i , the range of y can be calculated as in References [3,35]. Randomly selecting 100 groups of data for the x i and the corresponding output y as the sample data set U = {x 1 , x 2 , x 3 , x 4 , y}.…”

Section: Case Study 1: Simulation Datamentioning

confidence: 99%

“…Yang et al studied the bridge risk assessment problem with an extended disjunctive BRB 37 . Although the disjunctive BRB could help avoid the combinatorial explosion problem, it was difficult to directly obtain a disjunctive BRB from either historical data or experts' knowledge 35,38 …”

Section: Introductionmentioning

confidence: 99%

“…The disjunctive BRB required far fewer rules in comparison with a conjunctive BRB in the same belief structure (with the same referential values for the same attribute). 35 Chang et al…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel nonlinear causal inference approach using vector‐based belief rule base

Zhang

Chen

et al. 2021

Int J of Intelligent Sys

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When using the belief rule base (BRB) methodology to deal with the nonlinear causal inference problems, combinatorial explosion often occurs due to overnumbered antecedent attributes, resulting in poor performance. Therefore, this paper proposes a novel nonlinear causal inference approach based on vector‐based BRB. In the modeling process of BRB, the original attributes are ranked by contribution rate and transformed into attribute vectors. Meanwhile, combined with the k‐means method, appropriate referential vectors are obtained. Thereby a vector‐based BRB can be established. In the inference process of BRB, the idea of full activation of vector‐based rules is presented. By calculating the spatial matching degree of the testing sample and the referential vectors, activation weights of the rules which are used in the evidential reasoning algorithm are acquired. Experimental results of a nonlinear function with four‐dimensional input and the pipeline leakage detection data show the effectiveness and superiority of the proposed approach.

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Section: Case Study 1: Simulation Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel nonlinear causal inference approach using vector‐based belief rule base

Zhang

Chen

et al. 2021

Int J of Intelligent Sys

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“…Two primary approaches have been presented to solve this issue. The first is to modify Dempster's rule of combination, based on which varieties of new rules have been proposed in Fei, Feng, and Liu, Li and Deng, Jiang, and Fei, Xia, Feng, and Liu and the second one focuses on improving the sources of evidence without modifying the combination rule . From other perspectives, such as divergence measure and evolutionary game theory, many approaches were put forward.…”

Section: Introductionmentioning

confidence: 99%

“…The first is to modify Dempster's rule of combination, based on which varieties of new rules have been proposed in and the second one focuses on improving the sources of evidence without modifying the combination rule. [14][15][16] From other perspectives, such as divergence measure 7 and evolutionary game theory, 17 many approaches were put forward.The combination of bodies of evidence could be employed in plenty of contexts where the knowledge of experts needs to be aggregated. One crucial application is environmental impact assessment (EIA), 18 which focuses on the recognition and assessment of possible influencing factors, including physical-chemistry, biology, culture, etc.…”

mentioning

confidence: 99%

On combination rule in Dempster–Shafer theory using OWA‐based soft likelihood functions and its applications in environmental impact assessment

Fei

2019

Int J Intell Syst

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Dempster–Shafer theory (DST) was presented as an effective mathematical tool to represent uncertainty. Its significant innovation is to allow the allocation of the belief of mass to sets or intervals, and it becomes a valuable method in the field of decision making and evaluation when accurate information is not available or when knowledge is expressed subjectively by humans. A crucial research issue in DST is the combination of multi‐sources of evidence. In this paper, a novel combination rule for Dempster–Shafer structures is developed based on ordered weighted average (OWA)‐based soft likelihood functions proposed by Yager. First, the belief intervals, including the belief measures and plausibility measures, of all the hypotheses in the frame of discernment (FOD) are calculated. Second, the representative value of belief interval is defined based on golden rule introduced by Yager. Third, the soft likelihood value of each hypothesis is calculated based on the proposed OWA‐based soft likelihood function for belief interval, which can be considered as the combined evidence. The final evaluation results can be employed for practical applications, such as decision making and evaluation. In addition, the improved evidence combination rule is presented which takes into account the weight of evidence. Several illustrative examples are conducted to manifest the use of the developed methods. Finally, an application for environmental impact assessment is given to demonstrate the usefulness of the developed combination rule in DST.

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A two‐phase features extraction approach for BRB based fault diagnosis of electromechanical system

Zhang,

Liu,

Xiao

et al. 2024

Adaptive Control & Signal

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SummaryBelief rule base (BRB) is an effective nonlinear relationship modeling approach. It has been widely used in the fault diagnosis of electromechanical systems. To improve the performance of the BRB‐based diagnostic model, a two‐phase features extraction approach called CNPCA based on complex network (CN) and principal component analysis (PCA) is proposed in this paper. In the first phase, the weighted visibility graph method is applied to transform the time series data of monitored variables into complex networks. Then the statistical attributes of the constructed networks are extracted as the initial features. In the second phase, the PCA method is used to process the initial features and the principal component features are obtained. After that, the CNPCA‐BRB diagnostic model for the electromechanical system is constructed. The experimental results of the elevator fault diagnosis show that the constructed diagnostic model outperforms better than the classical ones. It demonstrates that the CNPCA approach can ensure the integrity of fault information in the features and improve the separability of the fault features.

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Indirect disjunctive belief rule base modeling using limited conjunctive rules: Two possible means

Cited by 16 publications

References 31 publications

A novel nonlinear causal inference approach using vector‐based belief rule base

A novel nonlinear causal inference approach using vector‐based belief rule base

On combination rule in Dempster–Shafer theory using OWA‐based soft likelihood functions and its applications in environmental impact assessment

A two‐phase features extraction approach for BRB based fault diagnosis of electromechanical system

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