Information Fusion Based on Information Entropy in Fuzzy Multi-source Incomplete Information System

Xu, Weihua; Li, Mengmeng; Wang, Xizhao

doi:10.1007/s40815-016-0230-9

Cited by 64 publications

(16 citation statements)

References 27 publications

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“…Thus, Pl(A) in Equation (8) reflects the unsuspicious degree of proposition A, which conforms to its definition in Equation (6). Obviously, Bel(A) and Pl(A) respectively denote the upper and lower bounds of probability, which form the uncertainty interval [Bel(A), Pl(A)] (shown in Figure 1).…”

Section: Uncertainty Quantizationmentioning

confidence: 71%

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Conflicting Information Fusion Based on an Improved DS Combination Method

Chen

Jiang

et al. 2017

Symmetry

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An effective and reliable fusion method for conflicting information is proposed in this paper. Compared with a single-sensor system, a multi-sensor fusion system can comprehensively combine the redundancy and complementarity of multi-sensor information to obtain better system performance. Hence, the multi-sensor fusion system has become one of the research hotspots. However, due to lack knowledge about the measurement environment and limited sensor accuracy, the multi-sensor system inevitably appears to have imperfect, uncertain and inconsistent information. To solve the problem, we introduce one powerful uncertainty reasoning method: Dempster-Shafer theory (DS theory). With convincing measurement and a forceful combination of uncertain information, DS theory is widely applied in various fields, like decision-making, expert systems, target tracking, monitoring systems, etc. Nevertheless, DS theory will produce counter-intuitive fusion results when the pieces of evidence are highly conflicting. To address this issue, we raise an improved DS combination method for conflicting information fusion in this paper. First of all, the modified Minkowski distance function and the betting-commitment distance function are separately employed to revise potentially conflicting pieces of evidence. The procedure availably solves the conflicting situations caused by unreliable and imprecise evidence sources, which enhances the consistency among pieces of evidence. Then, based on two revised pieces of evidence, a conflicting redistribution strategy based on locally conflicting analyses is put forward. The approach dexterously combines two revised pieces of evidence to avoid conflicting situations caused by compulsive normalization, which further improves the accuracy and convergence speed of the multi-sensor fusion system. Finally, two experimental analyses with consistent information and conflicting information reveal the remarkable effectiveness and priority of the proposed algorithm for the multi-sensor fusion system. Consequently, this paper has certain value for the multi-sensor fusion system.

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Section: Uncertainty Quantizationmentioning

confidence: 71%

“…However, because of different sensor sensitivities in the multi-sensor system and various potential deception and interference in a hostile environment, the multi-sensor information provided by multi-sensor sources is normally imperfect, uncertain and inconsistent [5,6].…”

Section: Introductionmentioning

confidence: 99%

Conflicting Information Fusion Based on an Improved DS Combination Method

Chen

Jiang

et al. 2017

Symmetry

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show abstract

“…Mathon et al [5] used the Dempster-Shafer evidence theory (DST) to fuse collected field data and expert judgement information and obtained a better model applied to uncertainty surrounding permeability. Xu et al [6] proposed a multisource fuzzy incomplete information fusion method based on the information entropy theory and verified its effectiveness. e methods mentioned above are not appropriate for building the reliability models of the CNC system because of highly complex calculations involved.…”

Section: Introductionmentioning

confidence: 99%

Developing a Reliability Model of CNC System under Limited Sample Data Based on Multisource Information Fusion

Peng

Cai

Liu

et al. 2020

Mathematical Problems in Engineering

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The reliability of the computer numerical control (CNC) system affects its processing performance and is a major concern in the manufacturing industry today. However, existing reliability models to assess the reliability of the CNC system often exhibit relatively large errors due to inadequate treatment of small samples. In order to get around the constraint of limited lifetime failure data and take full advantage of existing reliability parameters in traditional reliability models, a multisource information fusion-based reliability model grounded on Bayesian inference is proposed to deal with the small sample size. The prior distributions are derived by using the probability encoding method and conjugate distribution based on the idea of multisource information fusion. Then, using the Jensen–Shannon divergence (JSD) to measure the similarity between prior information and field observation data, a constrained optimization problem is established to determine the respective weight of prior information and field observation data. Finally, by conducting the reliability analysis of repairable CNC systems, the validity of the proposed model and its prior distribution derivation method are verified.

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“…In 1997, the same country established the Institute of Information Fusion and organized a number of special projects to study data fusion technology. With recent rapid development in computer and network communication technology as well as the growing need for military applications, information fusion technology has seen remarkable achievements [9] , and its application areas have gradually expanded from the initial military field to other areas, such as intelligent robotics, image processing and analysis, earth science, agricultural applications, weather forecasting, modern manufacturing industries, and economic management [10] . However, the modeling mechanisms and application conditions of these technologies differ, and each method presents some limitations for certain prediction problems in various applications.…”

Section: Introductionmentioning

confidence: 99%

An information fusion model of innovation alliances based on the bayesian network

Xia

Feng

Liu

et al. 2018

Tinshhua Sci. Technol.

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To solve the problem of information fusion from multiple sources in innovation alliances, an information fusion model based on the Bayesian network is presented. The multi-source information fusion process of innovation alliances was classified into three layers, namely, the information perception layer, the feature clustering layer, and the decision fusion layer. The agencies in the alliance were defined as sensors through which information is perceived and obtained, and the features were clustered. Finally, various types of information were fused by the innovation alliance based on the fusion algorithm to achieve complete and comprehensive information. The model was applied to a study on economic information prediction, where the accuracy of the fusion results was higher than that from a single source and the errors obtained were also smaller with the MPE less than 3%, which demonstrates the proposed fusion method is more effective and reasonable. This study provides a reasonable basis for decision-making of innovation alliances.

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Information Fusion Based on Information Entropy in Fuzzy Multi-source Incomplete Information System

Cited by 64 publications

References 27 publications

Conflicting Information Fusion Based on an Improved DS Combination Method

Conflicting Information Fusion Based on an Improved DS Combination Method

Developing a Reliability Model of CNC System under Limited Sample Data Based on Multisource Information Fusion

An information fusion model of innovation alliances based on the bayesian network

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