Journal article Open Access

An evidence clustering DSmT approximate reasoning method for more than two sources

QiangGuo; You He; Tao Jian; Haipeng Wang; Shutao Xia

Due to the huge computation complexity of Dezert–Smarandache Theory (DSmT), its applications especially for multi-source (more than two sources) complex fusion problems have been limited. To get high similar approximate reasoning results with Proportional Conflict Redistribution 6 (PCR6) rule in DSmT framework (DSmT +PCR6) and remain less computation complexity, an Evidence Clustering DSmT approximate reasoning method for more than two sources is proposed. Firstly, the focal elements of multi evidences are clustered to two sets by their mass assignments respectively. Secondly, the convex approximate fusion results are obtained by the new DSmT approximate formula for more than two sources. Thirdly, the final approximate fusion results by the method in this paper are obtained by the normalization step. Analysis of computation complexity show that the method in this paper cost much less computation complexity than DSmT +PCR6. The simulation experiments show that the method in this paper can get very similar approximate fusion results and need much less computing time than DSmT +PCR6, especially, when the numbers of sources and focal elements are large, the superiorities of the method are remarkable.

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