Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (7): 2140-2146.DOI: 10.11772/j.issn.1001-9081.2022060867
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Sirui WANG, Shijuan CHENG(), Feimeng YUAN
Received:
2022-06-16
Revised:
2022-09-24
Accepted:
2022-09-26
Online:
2022-10-08
Published:
2023-07-10
Contact:
Shijuan CHENG
About author:
WANG Sirui, born in 1998, M. S. candidate. Her research interests include reliability theory and engineering, information fusion.Supported by:
通讯作者:
程世娟
作者简介:
王思蕊(1998—),女,四川绵阳人,硕士研究生,主要研究方向:可靠性理论与工程、信息融合;基金资助:
CLC Number:
Sirui WANG, Shijuan CHENG, Feimeng YUAN. Reliability evaluation method of high-reliability products based on improved evidence fusion[J]. Journal of Computer Applications, 2023, 43(7): 2140-2146.
王思蕊, 程世娟, 袁非梦. 基于改进证据融合的高可靠产品可靠性评估方法[J]. 《计算机应用》唯一官方网站, 2023, 43(7): 2140-2146.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022060867
1.914 1 | 0.978 4 | 0.265 6 | ||
2.528 0 | 0.740 8 | 0.201 1 | ||
1.872 8 | 1.000 0 | 0.271 4 | ||
1.941 3 | 0.964 7 | 0.261 9 |
Tab. 1 Similarity weights defined based on consistency of evidence at credal level
1.914 1 | 0.978 4 | 0.265 6 | ||
2.528 0 | 0.740 8 | 0.201 1 | ||
1.872 8 | 1.000 0 | 0.271 4 | ||
1.941 3 | 0.964 7 | 0.261 9 |
1.561 7 | 0.357 3 | ||
0.074 0 | 0.016 9 | ||
1.567 4 | 0.357 4 | ||
1.177 3 | 0.268 4 |
Tab. 2 Similarity weights defined based on consistency of evidence at pignistic level
1.561 7 | 0.357 3 | ||
0.074 0 | 0.016 9 | ||
1.567 4 | 0.357 4 | ||
1.177 3 | 0.268 4 |
1.537 8 | 0.288 5 | 1.250 9 | 0.354 7 | ||
2.875 0 | 0.154 3 | 2.191 8 | 0.202 4 |
Tab. 3 Reliability weights defined based on evidence uncertainty
1.537 8 | 0.288 5 | 1.250 9 | 0.354 7 | ||
2.875 0 | 0.154 3 | 2.191 8 | 0.202 4 |
方法 | 各命题BPA | |||||
---|---|---|---|---|---|---|
m( | m( | m( | m( | m( | m( | |
传统D-S方法 | 0.076 9 | 0.028 9 | 0.124 9 | 0.041 7 | 0.717 8 | 0.009 8 |
基于信度层证据一致性修正 | 0.097 7 | 0.123 5 | 0.031 6 | 0.114 7 | 0.183 8 | 0.448 7 |
基于决策层证据一致性修正 | 0.006 3 | 0.166 9 | 0.061 1 | 0.149 6 | 0.207 5 | 0.408 6 |
基于证据不确定度修正 | 0.070 0 | 0.134 9 | 0.048 8 | 0.159 5 | 0.146 4 | 0.440 4 |
文献[ | 0.090 4 | 0.127 6 | 0.033 3 | 0.116 7 | 0.185 0 | 0.447 0 |
文献[ | 0.060 6 | 0.132 2 | 0.042 8 | 0.142 0 | 0.184 1 | 0.438 3 |
本文方法 | 0.027 5 | 0.155 5 | 0.055 8 | 0.151 3 | 0.187 8 | 0.422 1 |
Tab. 4 BPA after fusing by different methods
方法 | 各命题BPA | |||||
---|---|---|---|---|---|---|
m( | m( | m( | m( | m( | m( | |
传统D-S方法 | 0.076 9 | 0.028 9 | 0.124 9 | 0.041 7 | 0.717 8 | 0.009 8 |
基于信度层证据一致性修正 | 0.097 7 | 0.123 5 | 0.031 6 | 0.114 7 | 0.183 8 | 0.448 7 |
基于决策层证据一致性修正 | 0.006 3 | 0.166 9 | 0.061 1 | 0.149 6 | 0.207 5 | 0.408 6 |
基于证据不确定度修正 | 0.070 0 | 0.134 9 | 0.048 8 | 0.159 5 | 0.146 4 | 0.440 4 |
文献[ | 0.090 4 | 0.127 6 | 0.033 3 | 0.116 7 | 0.185 0 | 0.447 0 |
文献[ | 0.060 6 | 0.132 2 | 0.042 8 | 0.142 0 | 0.184 1 | 0.438 3 |
本文方法 | 0.027 5 | 0.155 5 | 0.055 8 | 0.151 3 | 0.187 8 | 0.422 1 |
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