Abstract
In this paper, we develop a high performance algorithm which is adapted to uncertainty computing and give a new combination rules coming from the D–S and supply a gap that Dempster ignoranced. The evidence sources are adapted in different cases. The credibility of the evidence changes along with the different focus element. So, we give various credibility for every focus element to increase precision. The new method improves the precision and gets rid of disconvergent answer.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yager R.: Using Approximate Reasoning to Represent Default Knowledge. Artificial Intelligence, 31(1), 99–112 (1987)
Dubois D., Prade H.: Default Reasoning and Possibility Theory. Artificial Intelligence, 35(2), 243–257 (1988)
Chen L., Rao S.S.: Modified Dempster-Shafer theory for multicriteria optimization. Engineering Optimization, 30(3/4), 177–201 (1998)
Murphy C.K.: Combining belief functions when evidence conflicts. Decision Support Systems, 29(1), 1–9 (2000)
Murphy, Robin R.: Dempster-Shafer theory for sensor fusion in autonomous mobile robots. IEEE Transactions on Robotics and Automation, 14(2), 197–206 (1998)
Lefevre E., Colot O.: Vannoorenberghe P. Belief function combination and conflict management. Information Fusion, 3(2), 149–162 (2002)
Murphy, Robin R.: Adaptive rule of combination for observations over time. in Proc of IEEE International Conference on Multi-sensor, Fusion and Integration for Intelligent Systems 1996 IEEE, Piscataway, NJ, USA, 125–131 (1996)
Pigeon Luc, Solaiman Bassel, Toutin Thierry etc. Dempster-Shafer theory for multi-satellites remotely-sensed observations. in Proceedings of SPIEThe International Society for Optical Engineering, SPIE, Bellingham, WA, USA. 4051, 228–236 (2000)
Wang Z., Hu W.D., Yu W.X.: A combination rule of conflict evidence based on proportional belief assignment, Acta Electronica Sinica, 29(z1):1852–1855 (2001)
Josang A.: The consensus operator for combining beliefs, Artificial Intelligence, 141(1–2), 157–170 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shi, S., Li, Q., Xu, L., Xia, D., Xia, X., Yu, G. (2005). A High Performance Algorithm on Uncertainty Computing. In: Zhang, W., Tong, W., Chen, Z., Glowinski, R. (eds) Current Trends in High Performance Computing and Its Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27912-1_58
Download citation
DOI: https://doi.org/10.1007/3-540-27912-1_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25785-1
Online ISBN: 978-3-540-27912-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)