Information Volume of Fuzzy Membership Function

Deng, Jixiang; Deng, Yong

doi:10.15837/ijccc.2021.1.4106

Cited by 117 publications

(40 citation statements)

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“…The unknown world is tens of thousands (Deng 2020;Deng and Deng 2021), and people cannot accurately judge the uncertainty of an unknown information (Pan et al 2019;Jiang et al 2019). But we can give an interval of the uncertainty of an object and then control the uncertainty of an object within a range, which is also a way of expressing unknown information.…”

Section: Golden Rule Methodsmentioning

confidence: 99%

Interval-valued belief entropies for Dempster–Shafer structures

Xue

Deng

2021

Soft Comput

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In practical application problems, the uncertainty of an unknown object is often very difficult to accurately determine, so Yager proposed the interval-valued entropies for Dempster-Shafer structures, which is based on Dempster-Shafer structures and classic Shannon entropy and is an interval entropy model. Based on Dempster-Shafer structures and classic Shannon entropy, the interval uncertainty of an unknown object is determined, which provides reference for theoretical research and provides help for industrial applications. Although the interval-valued entropies for Dempster-Shafer structures can solve the uncertainty interval of an object very efficiently, its application scope is only a traditional probability space. How to extend it to the evidential environment is still an open issue. This paper proposes interval-valued belief entropies for Dempster-Shafer structures, which is an extension of the interval-valued entropies for Dempster-Shafer structures. When the belief entropy degenerates to the classic Shannon entropy, the interval-valued belief entropies for Dempster-Shafer structures will degenerate into the interval-valued entropies for Dempster-Shafer structures. Numerical examples are applied to verify the validity of the interval-valued belief entropies for Dempster-Shafer structures. The experimental results demonstrate that the proposed entropy can obtain the interval uncertainty value of a given uncertain object successfully and make decision effectively.

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Section: Golden Rule Methodsmentioning

confidence: 99%

Interval-valued belief entropies for Dempster–Shafer structures

Xue

Deng

2021

Soft Comput

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“…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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“…A chemical tree is a tree with d v ≤ 4 for all v ∈ V ( G ). For details on chemical trees, see References [20–25]. Let

T_{n}

(

{CT}_{n}

) be the set of trees (chemical trees) with n vertices.…”

Section: Introductionmentioning

confidence: 99%

More on Sombor indices of chemical graphs and their applications to the boiling point of benzenoid hydrocarbons

Liu

Chen

Xiao

et al. 2021

Int J of Quantum Chemistry

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Let G be a connected graph with vertex set V(G) and edge set E(G). The Sombor index of G is defined asRecent results of Sombor index can be found in References [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].A chemical tree is a tree with d v ≤ 4 for all v ∈ V(G). For details on chemical trees, see . Let T n (CT n ) be the set of trees (chemical trees) with n vertices. Let T n,k (CT n,k ) be the set of trees (chemical trees) with n vertices and k pendent vertices.

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Information Volume of Fuzzy Membership Function

Cited by 117 publications

References 0 publications

Interval-valued belief entropies for Dempster–Shafer structures

Interval-valued belief entropies for Dempster–Shafer structures

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

More on Sombor indices of chemical graphs and their applications to the boiling point of benzenoid hydrocarbons

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