Toward uncertainty of weighted networks: An entropy-based model

Yin, Likang; Deng, Yong

doi:10.1016/j.physa.2018.05.067

Cited by 66 publications

(28 citation statements)

References 62 publications

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“…Many math tools are presented to model uncertainty, such as Z numbers, D numbers, and entropy model . Among then, evidence theory is widely used due to its efficiency to deal with uncertainty.…”

Section: Preliminariesmentioning

confidence: 99%

“…Many math tools are presented to model uncertainty, such as Z numbers, [53][54][55] D numbers, [56][57][58][59][60][61] and entropy model. [62][63][64][65][66][67] Among then, evidence theory is widely used due to its efficiency to deal with uncertainty. In this section, the traditional D-S evidence, 1 the Pearson correlation coefficient, 52 the Spearman correlation coefficient, 68 and the DEMATEL method are introduced.…”

Section: Preliminariesmentioning

confidence: 99%

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Dependent evidence combination based on decision‐making trial and evaluation laboratory method

Deng

2019

Int J Intell Syst

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Dempster‐Shafer is widely used to address the problems of uncertainty. One assumption mentioned in this theory is that the distribution of information should be independent. In practice, the requirement cannot be fulfilled. One of the efficient methods to deal with dependent evidence is to calculate the correlation discounting. However, existing coefficient can only be applied to show the direct relation between evidence A and B but do not take the indirect relationship into consideration. To address this issue, in this paper, a new method to combine dependent evidence based on decision‐making trial and evaluation laboratory is presented, not only considering the relation between evidence A and B and the relation between evidence B and C, but also considering the transitive influence between evidence A and C. Finally, the experiments on some benchmark data sets are illustrated to show the efficiency of the proposed method.

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Section: Preliminariesmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Dependent evidence combination based on decision‐making trial and evaluation laboratory method

Deng

2019

Int J Intell Syst

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“…Handling uncertainty in complex systems is still an open issue . Many math tools, such as fuzzy sets, Z‐numbers, and D numbers have been presented to deal with many engineering applications.…”

Section: Preliminariesmentioning

confidence: 99%

A new divergence measure for basic probability assignment and its applications in extremely uncertain environments

Fei

Deng

2018

Int J Intell Syst

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Information fusion under extremely uncertain environments is an important issue in pattern classification and decision‐making problems. The Dempster‐Shafer evidence theory (D‐S theory) is more and more extensively applied in dealing with uncertain information. However, the results contrary to common sense are often obtained when combining different evidence using Dempster's combination rule. How to measure the difference between different evidence is still an open issue. In this paper, a new divergence is proposed based on the Kullback‐Leibler divergence to measure the difference between different basic probability assignments (BPAs). Numerical examples are used to illustrate the computational process of the proposed divergence. Then, the similarity for different BPAs is also defined based on the proposed divergence. The basic knowledge about pattern recognition is introduced, and a new classification algorithm is presented using the proposed divergence and similarity under extremely uncertain environments. The effectiveness of the classification algorithm is illustrated by a small example handling robot sensing. The proposed method is motivated by the urgent need to develop intelligent systems, such as sensor‐based data fusion manipulators, which are required to work in complicated, extremely uncertain environments. Sensory data satisfy the conditions (1) fragmentary and (2) collected from multiple levels of resolution.

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“…At the same time, some scholars have studied other areas of complex networks based on key node identification methods. For example, Wei and Deng used the closeness centrality method to improve the cluster‐growing method for measuring the fractal dimension of a network; Yin and Deng proposed a method to measure the uncertainty of nodes in a weighted network that draws on the ideas of node centrality and node strength in considering the local information of nodes. However, few studies have identified the influential parts of a CMP based on the key node identification methods of complex networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of the influential parts in a complex mechanical product from a reliability perspective using complex network theory

Yin

Dong

et al. 2019

Quality & Reliability Eng

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Due to the difference in the numbers and strengths of physical relationships among parts, complex mechanical products (CMPs) have community structure characteristics. There are often some influential parts in the community. Failures of these influential parts spread rapidly along the physical relationships between parts in the community, which seriously affects the reliability of a product. Therefore, identifying the influential parts in the community and adopting targeted measures can effectively improve the reliability and service life of a product. However, identifying the influential parts within each community in a collection of parts with complex relationships is very difficult. Thus, from the perspective of reliability, a method for identifying the influential parts of a CMP based on complex network theory is proposed and used to identify the influential parts in each community of products. First, weighted complex network (WCN) theory is employed to construct a CMP into a WCN model. Second, the complex network community detection method is employed to detect the community structure of the WCN model. Third, a modified LocalRank algorithm is employed to identify the influential nodes in each community, ie, the influential parts in each community of a CMP. Fourth, a modified susceptible‐infectious‐recovered (SIR) model is employed to evaluate the impacts of the influential parts. An analysis of a company's DC drill planetary gearbox shows that the proposed method is accurate and effective.

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Toward uncertainty of weighted networks: An entropy-based model

Cited by 66 publications

References 62 publications

Dependent evidence combination based on decision‐making trial and evaluation laboratory method

Dependent evidence combination based on decision‐making trial and evaluation laboratory method

A new divergence measure for basic probability assignment and its applications in extremely uncertain environments

Identification of the influential parts in a complex mechanical product from a reliability perspective using complex network theory

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