An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability Assignment
Uncertainty in data fusion applications has received great attention. Due to the effectiveness and flexibility in handling uncertainty, Dempster–Shafer evidence theory is widely used in numerous fields of data fusion. However, Dempster–Shafer evidence theory cannot be used directly for conflicting s...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2020/1594967 |
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| author | Yong Chen Yongchuan Tang Yan Lei |
| author_facet | Yong Chen Yongchuan Tang Yan Lei |
| author_sort | Yong Chen |
| collection | DOAJ |
| description | Uncertainty in data fusion applications has received great attention. Due to the effectiveness and flexibility in handling uncertainty, Dempster–Shafer evidence theory is widely used in numerous fields of data fusion. However, Dempster–Shafer evidence theory cannot be used directly for conflicting sensor data fusion since counterintuitive results may be attained. In order to handle this issue, a new method for data fusion based on weighted belief entropy and the negation of basic probability assignment (BPA) is proposed. First, the negation of BPA is applied to represent the information in a novel view. Then, by measuring the uncertainty of the evidence, the weighted belief entropy is adopted to indicate the relative importance of evidence. Finally, the ultimate weight of each body of evidence is applied to adjust the mass function before fusing by the Dempster combination rule. The validity of the proposed method is demonstrated in accordance with an experiment on artificial data and an application on fault diagnosis. |
| format | Article |
| id | doaj-art-e09f3f79ef224a95a3eec510f3d4333c |
| institution | OA Journals |
| issn | 2314-4629 2314-4785 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-e09f3f79ef224a95a3eec510f3d4333c2025-08-20T02:21:30ZengWileyJournal of Mathematics2314-46292314-47852020-01-01202010.1155/2020/15949671594967An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability AssignmentYong Chen0Yongchuan Tang1Yan Lei2School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, ChinaSchool of Big Data and Software Engineering, Chongqing University, Chongqing 401331, ChinaSchool of Big Data and Software Engineering, Chongqing University, Chongqing 401331, ChinaUncertainty in data fusion applications has received great attention. Due to the effectiveness and flexibility in handling uncertainty, Dempster–Shafer evidence theory is widely used in numerous fields of data fusion. However, Dempster–Shafer evidence theory cannot be used directly for conflicting sensor data fusion since counterintuitive results may be attained. In order to handle this issue, a new method for data fusion based on weighted belief entropy and the negation of basic probability assignment (BPA) is proposed. First, the negation of BPA is applied to represent the information in a novel view. Then, by measuring the uncertainty of the evidence, the weighted belief entropy is adopted to indicate the relative importance of evidence. Finally, the ultimate weight of each body of evidence is applied to adjust the mass function before fusing by the Dempster combination rule. The validity of the proposed method is demonstrated in accordance with an experiment on artificial data and an application on fault diagnosis.http://dx.doi.org/10.1155/2020/1594967 |
| spellingShingle | Yong Chen Yongchuan Tang Yan Lei An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability Assignment Journal of Mathematics |
| title | An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability Assignment |
| title_full | An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability Assignment |
| title_fullStr | An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability Assignment |
| title_full_unstemmed | An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability Assignment |
| title_short | An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability Assignment |
| title_sort | improved data fusion method based on weighted belief entropy considering the negation of basic probability assignment |
| url | http://dx.doi.org/10.1155/2020/1594967 |
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