An Evidence Clustering Dsmt Approximate Reasoning Method Based On Convex Functions Analysis
Qiang Guo, HE You, GUAN Xin, DENG Li, PAN Lina, JIAN Tao · Zenodo (CERN European Organization for Nuclear Research) · 2015
With the increasing number of focal elements in frame of discernment, computational complexity of DSmT(Dezert-Smarandache Theory) increases exponentially, which blocks the wide application and development of DSmT. To solve this problem, a new evidence clustering DSmT approximate reasoning method is proposed in this paper based on convex functions analysis. The computational complexity of the method in this paper increases linearly instead of exponentially with the increasing number of focal elements in discernment framework. First, the method clusters the belief masses of focal elements in each evidence. Then, the first step results are obtained by the proposed DSmT approximate convex functions formula. Finally, the method gets the approximate fusion results by normalization method. The results of simulation show that the approximate fusion results of the method in this paper has higher Euclidean similarity with the exact fusion results of DSmT+PCR5, and need less computational complexity than the existing approximate methods. Especially, in the case of large data and complex fusion problems, the method in this paper can get highly accurate results and need low computation complexity.