Sensor dynamic reliability evaluation based on evidence theory and intuitionistic fuzzy sets

Song, Yafei; Wang, Xiaodan; Zhu, Jingwei; Lei, Lei

doi:10.1007/s10489-018-1188-0

Cited by 113 publications

(72 citation statements)

References 33 publications

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“…In the diagnostic process of medical profession, improving the processing capacity of various uncertain and inconsistent information and achieving more accurate decision-making have become the major challenges in the development of medical diagnosis [1][2][3]. Up to now, the process of medical diagnosis is driven by various theoretical studies, such as fuzzy sets theory [4][5][6], intuitionistic fuzzy sets [7][8][9][10], interval-valued intuitionistic fuzzy sets [11][12][13], and quantum decision [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A New Divergence Measure of Pythagorean Fuzzy Sets Based on Belief Function and Its Application in Medical Diagnosis

2020

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As the extension of the fuzzy sets (FSs) theory, the intuitionistic fuzzy sets (IFSs) play an important role in handling the uncertainty under the uncertain environments. The Pythagoreanfuzzy sets (PFSs) proposed by Yager in 2013 can deal with more uncertain situations than intuitionistic fuzzy sets because of its larger range of describing the membership grades. How to measure the distance of Pythagorean fuzzy sets is still an open issue. Jensen–Shannon divergence is a useful distance measure in the probability distribution space. In order to efficiently deal with uncertainty in practical applications, this paper proposes a new divergence measure of Pythagorean fuzzy sets, which is based on the belief function in Dempster–Shafer evidence theory, and is called PFSDM distance. It describes the Pythagorean fuzzy sets in the form of basic probability assignments (BPAs) and calculates the divergence of BPAs to get the divergence of PFSs, which is the step in establishing a link between the PFSs and BPAs. Since the proposed method combines the characters of belief function and divergence, it has a more powerful resolution than other existing methods. Additionally, an improved algorithm using PFSDM distance is proposed in medical diagnosis, which can avoid producing counter-intuitive results especially when a data conflict exists. The proposed method and the magnified algorithm are both demonstrated to be rational and practical in applications.

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Section: Introductionmentioning

confidence: 99%

A New Divergence Measure of Pythagorean Fuzzy Sets Based on Belief Function and Its Application in Medical Diagnosis

2020

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“…First of all, the multi-agent system has a better battlefield situational awareness than the single-agent system. It can obtain global situational information and has the ability of parallel perception and parallel charge decision-making [21]. Secondly, the ability of a cooperative agent system is stronger than that of a single agent, so a multi-agent system has excellent expansibility and robustness.…”

Section: Introductionmentioning

confidence: 99%

Study on Master Slave Interaction Model Based on Stackelberg Game in Distributed Environment

et al. 2020

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In view of the problems such as low efficiency, difficulty in resolving local conflicts and lack of practical application scenarios, existing in the interaction model of multi-agent systems in a distributed environment, a multi-master multi-slave interaction model was designed based on the Stackelberg game, which is applied to the interaction game problem between the controller and the participant in the command and control process. Through optimizing the Stackelberg game model and multi-attribute decision-making, the multi-master, multi-slave, multi-agent system of the Stackelberg game was designed, and the closed loop problem under the Stackelberg game is solved for dimension reduction and optimal function value. Finally, through the numerical derivation simulation and the training results of related system data, the high efficiency and strong robustness of the model were verified from multiple perspectives, and this model algorithm was proved to be true and highly efficient.

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“…The D‐S evidence theory was first proposed by Dempster in 1967 and later developed by his student Shafer in 1976 as an imprecise reasoning theory, which is widely promoted . The theory was first applied to expert systems, and it was gradually applied in the fields of information fusion, intelligence analysis, evidential reasoning, and multiattribute decision analysis . As an indeterminate reasoning method which satisfies the conditions weaker than Bayesian probability theory; it has the ability to directly express “uncertainty” and “don't know.” Therefore, the D‐S evidence theory is flexible and convenient for describing uncertainties.…”

Section: Introductionmentioning

confidence: 99%

An association coefficient of a belief function and its application in a target recognition system

Pan

Deng

2019

Int J Intell Syst

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The conflict problem in D-S evidence theory has attracted the attention of many scholars. Conflict coefficients are proposed to describe conflicts between bodies of evidence. The association coefficient as the opposite of the conflict coefficient is also used to measure the conflict. The larger the association coefficient, the smaller the conflict degree, and the higher the similarity between the evidence bodies, and vice versa.In this paper, the degree of association is defined by Deng Entropy, and a new association coefficient is proposed based on the basic inequality. The nature of the new association coefficient and conflict coefficients is explored using examples. Finally, the association coefficient combined with the D-S combination rule is applied to the target recognition system, and accurate results are obtained.association coefficient, conflict coefficients, conflict problem, Deng entropy, D-S evidence theory, target recognition system | INTRODUCTIONThe D-S evidence theory was first proposed by Dempster 1 in 1967 and later developed by his student Shafer 2 in 1976 as an imprecise reasoning theory, which is widely promoted. [3][4][5][6] The theory was first applied to expert systems, 7-9 and it was gradually applied in the fields of information fusion, 10-12 intelligence analysis, 9,13-16 evidential reasoning, [17][18][19] and multiattribute decision analysis. [20][21][22][23][24] As an indeterminate reasoning method which satisfies the conditions weaker than Bayesian probability theory; it has the ability to directly express "uncertainty" 25 and "don't know." 26 Therefore, the D-S evidence theory is flexible and convenient for describing uncertainties. However, D-S evidence theory has produced counterintuitive results in the fusion of a highly conflicting example proposed by Zadeh. 27 Therefore, the issue of conflict between bodies of evidence has attracted more and more researchers so far. [28][29][30][31][32] Domestic and foreign scholars have proposed a large number of improved methods, which can be divided into two major categories: (a) modification of the combination rules of the classical D-S evidence theory to achieve the redistribution of conflicts in the case of evidence conflicts. [33][34][35][36] Such measures alleviate the conflict between the bodies of evidence to a certain extent, but at the same time, it is difficult for the new rules to maintain the original properties of the D-S combination rules. (b) Keep the classic combination rules unchanged, and preprocess conflict data before combination. [37][38][39][40][41][42] Haenni 43 believes that the modification of the data model is in engineering, mathematics and philosophically more reasonable. However, regardless of the type of method used to deal with conflicts between evidence, the conflict coefficient is an extremely important parameter. Therefore, the most critical step before choosing the right combination of evidence is the measure of evidence conflict.To measure the conflict between evidence bodies, many researchers have proposed...

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Sensor dynamic reliability evaluation based on evidence theory and intuitionistic fuzzy sets

Cited by 113 publications

References 33 publications

A New Divergence Measure of Pythagorean Fuzzy Sets Based on Belief Function and Its Application in Medical Diagnosis

A New Divergence Measure of Pythagorean Fuzzy Sets Based on Belief Function and Its Application in Medical Diagnosis

Study on Master Slave Interaction Model Based on Stackelberg Game in Distributed Environment

An association coefficient of a belief function and its application in a target recognition system

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