Weighted Evidence Combination Based on Distance of Evidence and Entropy Function
Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a new weighted evidence combination on the basis of the distance between evidence and entropy function is presented. The proposed approach is identified as two procedures. First, the weigh...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-07-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/155014773218784 |
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| _version_ | 1832559137295171584 |
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| author | Jianwei Wang Fuyuan Xiao Xinyang Deng Liguo Fei Yong Deng |
| author_facet | Jianwei Wang Fuyuan Xiao Xinyang Deng Liguo Fei Yong Deng |
| author_sort | Jianwei Wang |
| collection | DOAJ |
| description | Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a new weighted evidence combination on the basis of the distance between evidence and entropy function is presented. The proposed approach is identified as two procedures. First, the weight is determined based on the distance of evidence. Then, the obtained weight value in the first step is modified by making advantage of Deng entropy function. Our proposed method can efficiently cope with high conflicting evidences with better performance of convergence. A numerical example is provided to demonstrate that the proposed method is reasonable and efficient in the end. |
| format | Article |
| id | doaj-art-5c8d253551a448d2afd95ed9f5ce0cd9 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2016-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-5c8d253551a448d2afd95ed9f5ce0cd92025-02-03T01:30:42ZengWileyInternational Journal of Distributed Sensor Networks1550-14772016-07-011210.1177/155014773218784Weighted Evidence Combination Based on Distance of Evidence and Entropy FunctionJianwei Wang0Fuyuan Xiao1Xinyang Deng2Liguo Fei3Yong Deng4 School of HanHong, Southwest University, Chongqing 400715, China School of Computer and Information Science, Southwest University, Chongqing 400715, China School of Computer and Information Science, Southwest University, Chongqing 400715, China School of Computer and Information Science, Southwest University, Chongqing 400715, China School of Engineering, Vanderbilt University, Nashville, TN 37235, USAConflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a new weighted evidence combination on the basis of the distance between evidence and entropy function is presented. The proposed approach is identified as two procedures. First, the weight is determined based on the distance of evidence. Then, the obtained weight value in the first step is modified by making advantage of Deng entropy function. Our proposed method can efficiently cope with high conflicting evidences with better performance of convergence. A numerical example is provided to demonstrate that the proposed method is reasonable and efficient in the end.https://doi.org/10.1177/155014773218784 |
| spellingShingle | Jianwei Wang Fuyuan Xiao Xinyang Deng Liguo Fei Yong Deng Weighted Evidence Combination Based on Distance of Evidence and Entropy Function International Journal of Distributed Sensor Networks |
| title | Weighted Evidence Combination Based on Distance of Evidence and Entropy Function |
| title_full | Weighted Evidence Combination Based on Distance of Evidence and Entropy Function |
| title_fullStr | Weighted Evidence Combination Based on Distance of Evidence and Entropy Function |
| title_full_unstemmed | Weighted Evidence Combination Based on Distance of Evidence and Entropy Function |
| title_short | Weighted Evidence Combination Based on Distance of Evidence and Entropy Function |
| title_sort | weighted evidence combination based on distance of evidence and entropy function |
| url | https://doi.org/10.1177/155014773218784 |
| work_keys_str_mv | AT jianweiwang weightedevidencecombinationbasedondistanceofevidenceandentropyfunction AT fuyuanxiao weightedevidencecombinationbasedondistanceofevidenceandentropyfunction AT xinyangdeng weightedevidencecombinationbasedondistanceofevidenceandentropyfunction AT liguofei weightedevidencecombinationbasedondistanceofevidenceandentropyfunction AT yongdeng weightedevidencecombinationbasedondistanceofevidenceandentropyfunction |