New evidence fusion method based on weighted mean value model

LU Shen-yi; SHUI Ai-she

doi:10.13873/J.1000-9787(2016)07-0055-03

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New evidence fusion method based on weighted mean value model

更新时间：2025-09-11

- New evidence fusion method based on weighted mean value model
- Vol. 35, Issue 7, Pages: 55-57+61(2016)
- 作者机构：
  
  后勤工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2016)07-0055-03
  CLC： TP202
- Published：2016
- 稿件说明：
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[1]路申易,税爱社.新的基于加权均值模型的证据融合方法[J].传感器与微系统,2016,35(07):55-57+61.

LU Shen-yi, SHUI Ai-she. New evidence fusion method based on weighted mean value model[J]. 2016, 35(7): 55-57+61.
[1]路申易,税爱社.新的基于加权均值模型的证据融合方法[J].传感器与微系统,2016,35(07):55-57+61. DOI： 10.13873/J.1000-9787(2016)07-0055-03.

LU Shen-yi, SHUI Ai-she. New evidence fusion method based on weighted mean value model[J]. 2016, 35(7): 55-57+61. DOI： 10.13873/J.1000-9787(2016)07-0055-03.

摘要

针对D-S证据理论难以处理证据冲突的问题

提出了一种将Murphy平均融合方法和证据权方法相结合的证据融合方法。该方法将显著偏差证据的判别引入融合流程

实现对证据权重的区分量化

建立了加权的基本概率分配均值模型。仿真结果表明:该方法能有效区分证据的重要程度

提高了证据融合的准确性与收敛速度

较好地解决了冲突证据融合的问题。

Abstract

Aiming at problem that evidence conflicting can’t be well solved by D-S evidence theory

a new evidence fusion method is proposed based on combination of Murphy average fusion method and weights of evidence method. This method introduces judgment of obvious deviation evidence into fusion process

weights of evidence are distinguish quantization

and mean value model for basic probability assignments of weighting is established. Simulation results show that the method can distinguish the importance of the evidence efficiently

accuracy of evidence fusion and convergence speed are also improved

solve problem of conflicting evidence very well.

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references

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D-S证据理论中冲突证据的合成方法 [J]. 刘海燕,赵宗贵,刘熹. 电子科技大学学报 . 2008(05)

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A novel fuzzy soft set approach in decision making based on grey relational analysis and Dempster–Shafer theory of evidence [J] . Hongxiang Tang. Applied Soft Computing . 2015

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Attribute reduction based on evidence theory in incomplete decision systems [J] . Wei-Zhi Wu. Information Sciences . 2007 (5)

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