Generate two-dimensional belief function based on an improved similarity measure of trapezoidal fuzzy numbers

Pelusi, Danilo; Deng, Yong

doi:10.1007/s40314-020-01371-9

Cited by 18 publications

(8 citation statements)

References 53 publications

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“…Definition Let hypothesis

A

be a subset of framework of discernment

normalΘ

. The sum of any subset corresponding to

A

is defined as belief function 59,60 :

B e l (A) = \sum_{B \subseteq A} m (B) .

Let hypothesis

A

be a subset of framework of discernment

normalΘ

. Nonnegative probability of

A

is defined as plausibility function:

P l (A) = \sum_{B \cap A = \emptyset} m (B) .

Since

B e l (A)

and

P l (A)

indicate the upper and lower probabilities of hypothesis

A

, it is clear that

P l (A) \geq B e l (A)

.…”

Section: Preliminariesmentioning

confidence: 99%

A generalized χ2 divergence for multisource information fusion and its application in fault diagnosis

Gao

Xiao

2021

Int J Intell Syst

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Dempster–Shafer theory is invaluable for handing uncertain problems in multisource information fusion field. But how to fuse highly conflicting information remains a pending issue. To deal with the issue, we propose a novel reinforced belief χ 2 divergence measure (named as MJX-tex-caligraphicnormalℛℬ χ 2 divergence) to calculate the conflict degree between evidence. The proposed MJX-tex-caligraphicnormalℛℬ χ 2 divergence comprehensively considers the effects of the single‐element subset and the multielement subset. In addition, the MJX-tex-caligraphicnormalℛℬ χ 2 divergence has been proved to be a bounded, nondegenerate, and symmetrical divergence measure. Then, we design a new MJX-tex-caligraphicnormalℛℬ χ 2 divergence‐based multisource information fusion method. This method combines information volume weights and supports degree weights to modify the evidence before fusion. Finally, an application for fault diagnosis is provided to show that the proposed method is superior to other existing methods.

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“…Definition Let hypothesis

A

be a subset of framework of discernment

normalΘ

. The sum of any subset corresponding to

A

is defined as belief function 59,60 :

B e l (A) = \sum_{B \subseteq A} m (B) .

Let hypothesis

A

be a subset of framework of discernment

normalΘ

. Nonnegative probability of

A

is defined as plausibility function:

P l (A) = \sum_{B \cap A = \emptyset} m (B) .

Since

B e l (A)

and

P l (A)

indicate the upper and lower probabilities of hypothesis

A

, it is clear that

P l (A) \geq B e l (A)

.…”

Section: Preliminariesmentioning

confidence: 99%

A generalized χ2 divergence for multisource information fusion and its application in fault diagnosis

Gao

Xiao

2021

Int J Intell Syst

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show abstract

“…After that, the information in Table 1 is processed using a gray fuzzy comprehensive evaluation model [27,28]. In this paper, the risk levels of different meteorological factors for transmission lines are divided into four types, i.e., G = {G 1 , G 2 , G 3 , G 4 }, which are associated with levels of "low risk", "general risk", "comparative high risk", and "high risk", respectively.…”

Section: Mlesnpsmentioning

confidence: 99%

A Fault Diagnosis Method Considering Meteorological Factors for Transmission Networks Based on P Systems

Chen

Wang

Ying

et al. 2021

Entropy

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Bad meteorological conditions may reduce the reliability of power communication equipment, which can increase the distortion possibility of fault information in the communication process, hence raising its uncertainty and incompleteness. To address the issue, this paper proposes a fault diagnosis method for transmission networks considering meteorological factors. Firstly, a spiking neural P system considering a meteorological living environment and its matrix reasoning algorithm are designed. Secondly, based on the topology structure of the target power transmission network and the action logic of its protection devices, a diagnosis model based on the spiking neural P system considering the meteorological living environment is built for each suspicious fault transmission line. Following this, the action messages of protection devices and corresponding temporal order information are used to obtain initial pulse values of input neurons of the diagnosis model, which are then modified with the gray fuzzy theory. Finally, the matrix reasoning algorithm of each model is executed in a parallel manner to obtain diagnosis results. Experiment results achieved out on IEEE 39-bus system show the feasibility and effectiveness of the proposed method.

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“…Meanwhile, the DRC meets commutative and associative laws [16,17]. Hence, DSET has been extensively researched, including the aspects of D numbers [18,19], evidential reasoning [20], heuristic representation learning [21], entropy [22,23], generation [24,25], dependency [26], the negation [27] of BBAs, etc. [12].…”

Section: Introductionmentioning

confidence: 99%

Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT

Xiao

2021

Sensors

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Multisource information fusion has received much attention in the past few decades, especially for the smart Internet of Things (IoT). Because of the impacts of devices, the external environment, and communication problems, the collected information may be uncertain, imprecise, or even conflicting. How to handle such kinds of uncertainty is still an open issue. Complex evidence theory (CET) is effective at disposing of uncertainty problems in the multisource information fusion of the IoT. In CET, however, how to measure the distance among complex basis belief assignments (CBBAs) to manage conflict is still an open issue, which is a benefit for improving the performance in the fusion process of the IoT. In this paper, therefore, a complex Pignistic transformation function is first proposed to transform the complex mass function; then, a generalized betting commitment-based distance (BCD) is proposed to measure the difference among CBBAs in CET. The proposed BCD is a generalized model to offer more capacity for measuring the difference among CBBAs. Additionally, other properties of the BCD are analyzed, including the non-negativeness, nondegeneracy, symmetry, and triangle inequality. Besides, a basis algorithm and its weighted extension for multi-attribute decision-making are designed based on the newly defined BCD. Finally, these decision-making algorithms are applied to cope with the medical diagnosis problem under the smart IoT environment to reveal their effectiveness.

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Generate two-dimensional belief function based on an improved similarity measure of trapezoidal fuzzy numbers

Cited by 18 publications

References 53 publications

A generalized χ2 divergence for multisource information fusion and its application in fault diagnosis

A generalized χ2 divergence for multisource information fusion and its application in fault diagnosis

A Fault Diagnosis Method Considering Meteorological Factors for Transmission Networks Based on P Systems

Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT

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