A novel belief rule base expert system with interval-valued references

Sun, Chao; Yang, Ruohan; He, Wei; Zhu, Hailong

doi:10.1038/s41598-022-10636-8

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…The P-CMA-ES algorithm is an improved algorithm based on the original CMA-ES algorithm, which is an excellent global optimization algorithm. In this paper, this algorithm can successfully solve the constraint problems in the BRB model and effectively optimize the experimental results [44].…”

Section: Figure 6: Brb Model Optimization Processmentioning

confidence: 99%

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Cheng¹,

Long²,

Wang³

et al. 2023

Computer Systems Science and Engineering

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Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base. The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model. However, due to the complexity of the milling system structure and the uncertainty of the milling failure index, it is often impossible to construct model expert knowledge effectively. Therefore, a milling system fault detection method based on fault tree analysis and hierarchical BRB (FTBRB) is proposed. Firstly, the proposed method uses a fault tree and hierarchical BRB modeling. Through fault tree analysis (FTA), the logical correspondence between FTA and BRB is sorted out. This can effectively embed the FTA mechanism into the BRB expert knowledge base. The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion. Secondly, evidence reasoning (ER) is used to ensure the transparency of the model reasoning process. Thirdly, the projection covariance matrix adaptation evolutionary strategies (P-CMA-ES) is used to optimize the model. Finally, this paper verifies the validity model and the method's feasibility techniques for milling data sets.

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Section: Figure 6: Brb Model Optimization Processmentioning

confidence: 99%

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Cheng¹,

Long²,

Wang³

et al. 2023

Computer Systems Science and Engineering

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show abstract

“…The basic concept behind the ER algorithm is based on evidence theory, also known as Dempster-Shafer theory. This theory provides a mathematical framework for reasoning under uncertainty and allows for the representation and manipulation of uncertain information [23]. In the ER algorithm, evidence is represented by confidence functions that assign degrees of belief to different hypotheses or states of the world.…”

Section: Reasoning Process Of the Modelmentioning

confidence: 99%

“…And then only the samples that are misclassified by the model are kept because they indicate some errors or limitations of the model that need to be corrected or improved. Finally, these samples are added to the training set and the model is trained again, as described in Equation (23).…”

Section: Err S Usablementioning

confidence: 99%

An online belief rule base breast cancer diagnosis method with interpretability

Han

et al. 2023

Preprint

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Early diagnosis of breast cancer is critical for effective treatment. Artificial intelligence (AI) technology has shown promise in assisting physicians with diagnosis. However, the combination of qualitative and quantitative information in surveillance data leads to ambiguity and uncertainty. Belief rule bases (BRB) can address these issues by incorporating confidence distributions. However, existing BRB models rely on offline training and lack adaptability to changes in patient metrics. In addition, the ethical implications of using BRB for breast cancer diagnosis require attention to the interpretability of the model. Therefore, this paper presents an online belief rule base breast cancer diagnosis method with interpretability. The method uses online learning to achieve dynamic growth. It also overcomes the problem of interpretability loss in the optimization process by implementing interpretability constraints. The proposed method achieves competitive accuracy and interpretability in breast cancer diagnosis, as demonstrated by experiments using a large dataset of breast cancer cases.

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“…The method of constructing a BRB by such intervals is also different from the traditional interval BRB. The traditional interval BRB only changes the reference point into the reference interval but does not change the rule combination method and the model reasoning process [42]. (2) In the reasoning process, the ER-rule is introduced, and the reliability of evidence is taken into account, which makes the results more accurate.…”

Section: Introductionmentioning

confidence: 99%

A Liquid Launch Vehicle Safety Assessment Model Based on Semi-Quantitative Interval Belief Rule Base

Cheng

Qian

et al. 2022

Mathematics

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As the propulsion part of a space launch vehicle and nuclear weapon missile, the health status of the liquid rocket determines whether the space launch vehicle and nuclear weapon missile can function normally. Therefore, it is of great significance to evaluate the health status of the liquid rocket. As the structure of the liquid rocket is becoming increasingly sophisticated, subjective judgment alone can no longer meet the needs of the actual system. As an expert system and a gray-box model, the belief rule base (BRB) can process both qualitative and quantitative information. The expert knowledge base is used in the safety assessment of a liquid rocket. However, in practical applications, the traditional BRB model still has two problems, which are that (1) when there are too many premise attributes, it easily leads to the explosion of combination rules, and (2) the reliability of rules is not considered in the process of model reasoning. Therefore, this paper proposes the BRB model with intervals (intervals-BRB) on the basis of traditional BRB. The interval-BRB retains the advantage of the traditional BRB, which can handle semi-quantitative information. In addition, the proposed model changes the reference point of the prerequisite attribute to the reference interval and changes the rule combination. This solves the problem of the traditional BRB explosive combination rule. The ER-rule (evidential reasoning rule) is introduced into the reasoning procedure, and the weight of the rule and the reliability of the rule are considered at the same time, which solves the shortcoming of the traditional BRB, which does not consider the reliability of the rule in reasoning. Finally, the CMAES optimization algorithm is used to optimize the initial model to obtain better performance. Finally, the model is verified by the actual data set of a liquid rocket, and the experimental results show that the model can achieve good experimental results.

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A novel belief rule base expert system with interval-valued references

Cited by 8 publications

References 34 publications

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

An online belief rule base breast cancer diagnosis method with interpretability

A Liquid Launch Vehicle Safety Assessment Model Based on Semi-Quantitative Interval Belief Rule Base

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