基于偏差的冲突证据融合的改进方法

Improvement of Fusion of Conflicting Evidence Based on Deviation

在Dempster-Shafer(D-S)证据理论中,传统D-S组合规则在证据高度冲突时存在失效问题,会产生与常理相悖的结论.为此,文中提出了一种改进的处理冲突证据的融合方法,即通过计算各证据到命题平均支持度的偏差,来检测和消除冲突证据,并对最后结果进行修正,以合成来自不同识别框架的证据.计算实验结果表明,该方法能有效处理冲突,得到符合实际的组合结果,相对于其他典型算法,该方法在收敛性和可靠性上有明显改进.

In the Dempster-Shafer evidence theory,the traditional D-S evidence combination rule fails to identify the actual conditions with highly conflicting evidence,which may result in contradicting conclusions.In order to solve this problem,this paper proposes an improved algorithm for the fusion of conflicting evidence,which detects and eliminates the conflicting evidence by calculating the deviation from all evidence to the average support level of the proposition,and amends the results to combine the evidence from different recognition frameworks.Numerical results show that the proposed method can effectively deal with the conflicts and achieve accurate identification,and that it is prior to other typical algorithms in terms of convergence and reliability.