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Erschienen in:
2011 | OriginalPaper | Buchkapitel
An Improved Particle Swarm Optimization for Uncertain Information Fusion
verfasst von : Peiyi Zhu, Benlian Xu, Baoguo Xu
Erschienen in: Advances in Swarm Intelligence
Verlag: Springer Berlin Heidelberg
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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.