Name
Abstract | ||
|---|---|---|
Multi-sensor information fusion is used to carry on synthesizing excellently to the multi-source information, make verdict of people more accurate and credible. But the influences of uncertainties on the safety/failure of the system and on the warranty costs exist. The new method to deal with the uncertain information fusion based on improved Dempster-Shafer (D-S) evidence theory has been proposed, and set up the concept of weight of sensor evidence itself and evidence distance based on a quantification of the similarity between sets to acquire the reliability weight of the relationship between evidences. Next an improved particle swarm optimization (PSO) is used to computer sensor weight to modify D-S evidence theory. Finally, numerical experiments are adopted to prove its effectiveness. |
Year | Venue | Keywords |
|---|---|---|
2011 | ICSI | evidence theory,sensor evidence,uncertain information fusion,computer sensor weight,evidence distance,improved particle swarm optimization,improved dempster-shafer,multi-sensor information fusion,d-s evidence theory,reliability weight,multi-source information,particle swarm optimization |
Field | DocType | Volume |
Particle swarm optimization,Data mining,Mathematical optimization,Computer science,Warranty,Multi-swarm optimization,Artificial intelligence,Information fusion,Machine learning | Conference | 6729 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peiyi Zhu | 1 | 21 | 5.78 |
| Benlian Xu | 2 | 81 | 20.96 |
| Baoguo Xu | 3 | 78 | 13.67 |