Improvement of Fusion Algorithm Based on Evidence Theory

被引：0

作者：

Chen Yi ^{[1
]}

Wang Gai-yun ^{[1
]}

Li Bing ^{[1
]}

机构：

[1] Inst Elect Ind, Dept Comp Sci, Guilin, Guangxi, Peoples R China

来源：

SECOND INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING: WGEC 2008, PROCEEDINGS | 2008年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The relationship between basic probability assignment (BPA) and belief function in evidence theory is studied. The idea that belief function is regarded as BPA in data fusion is firstly introduced in this paper. An improved fusion algorithm based on distributed fusion method is put forward using this idea. The improved algorithm is called 'distributed fusion algorithm based on belief function assignment'. Through the experimental simulations to the traditional distributed fusion algorithm and the improved algorithm, the results show that the two algorithms are both valid in property identification.

引用

页码：539 / 542

页数：4