An improved particle swarm optimization for uncertain information fusion

Authors:
Peiyi Zhu;Benlian Xu;Baoguo Xu
Affiliations:
College of IoT Engineering, Jiangnan University, Wuxi City, Jiangsu, China and School of Electrical and Automation Engineering, Changshu Institute of Technology, Changshu, Jiangsu, China;School of Electrical and Automation Engineering, Changshu Institute of Technology, Changshu, Jiangsu, China;College of IoT Engineering, Jiangnan University, Wuxi City, Jiangsu, China
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Year:
2011

Citing 2
Cited 0

On the Dempster-Shafer framework and new combination rules

Information Sciences: an International Journal
Fusion Method for Uncertain Multi-sensor Information

ICICTA '08 Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation - Volume 01

Quantified Score

Hi-index	0.00

Visualization

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.