摘要
该文对Dempster-Shafer(DS)理论以及Dezert-Smarandache理论(DSm T)进行了深入研究,为了能够在仅需较低计算复杂度的前提下得到更加精确的融合结果,提出一种新的快速DSm T-DS近似推理融合方法。该方法针对超幂集空间仅单子焦元具有信度赋值的情况,将超幂集空间拆分映射成元素为各单子焦元和其补集的二元集合的新的超幂集空间,并求出每个补集的信度赋值;再运用Dezert-Smarandache框架中的第5条比例冲突分配规则(DSm T+PCR5)在新的超幂集空间的二元集合子空间下对多证据源进行融合,得到各单子焦元的融合结果;然后通过归一化处理求得各单子焦元的信度赋值。通过理论分析得出该文方法的融合结果是介于Dezert-Smarandache框架中的第5条比例冲突分配规则(DSm T+PCR5)及Dempster-Shafer(DS)框架下的Dempster组合规则之间。该文方法在需要较低计算复杂度的前提下,可以得到优于Dempster组合规则的近似融合结果。最后通过多个角度与已有方法进行对比,验证了该文方法的优越性。
In this paper, Dempster-Shafer（DS） theory and Dezert-Smarandache Theory（DSm T） are conducted thorough reasearch, and in order to obtain more accurate fusion results in the premise of needing less computation complexity, a fast DSm T-DS approximate reasoning method is proposed. This method is only fit for the case that there are only singleton focal elements with assignments in hyper-power set. The hyper-power set is splitted and mapped to a new hyper-power set which consists of the binary sets of the focal element and its complementary set to the assignments of the complementary sets are computed. Proportional Conflict Redistribution No.5 within Dezert-Smarandache framework（DSm T＋PCR5） is applied to fuse the multi-source evidence in the binary sets of the new hyper-power set to get the fusion results of singleton focal elements. Then the assignments of singleton focal elements are obtained by normalization. Through the theoretical analysis, the conclusion is drawn that the fusion results of the mothod in this paper is between the results of DSm T＋PCR5 and Dempster＇s combination rule based on DS model, and the fusion results of the method in this paper which is better than the rusults of Dempster＇s combination rule can be obtained in the premise of minimal computation complexity. Finally, by comparing the method in this paper with the existing methods from different views, the superiority of new one is testified well.
出处
《电子与信息学报》
EI
CSCD
北大核心
2015年第9期2040-2046,共7页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61102166
61471379)
山东省优秀中青年科学家科研奖励基金(BS2013DX003)资助课题
