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Applications of Plausible, Paradoxical, and Neutrosophical Reasoning for Information Fusion (The Sixth International Conference on Information Fusion)
July 8-11, 2003
Radison Plaza Hotel at the Pier
Cairns, Queensland, Australia

Organizers
Chair: Dr. Jean Dezert, Co-Chair: Dr. Florentin Smarandache; Sponsors: Fusion 2003 Organizers, ONERA (France), University of New Mexico (USA)

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A Generalized Belief Fusion Algorithm
by
John Sudano
Lockheed Martin, Moorestown, NJ, 08057, USA

For a given body of belief evidential data, a processing objective is to glean the most correct knowledge from such data (but not more). The author introduced a new methodology of combining independent multi-source beliefs in 2002. This generalized belief fusion algorithm depending on the probability proportionally weighting functions used is shown to be equivalent to the Dempster-Shafer (DS) theory of evidence, the Modified Dempster-Shafer (MDS) theory and other fusion methodologies that will converge faster to correct results. A more computationally friendl representation of the generalized belief fusion algorithm is given.

Date received: February 19, 2003

Copyright © 2003 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # cajx-12.