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International Journal of Intelligent Systems
Article . 2019 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
DBLP
Article . 2020
Data sources: DBLP
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Quantum model of mass function

Authors: Xiaozhuan Gao; Yong Deng 0001;

Quantum model of mass function

Abstract

Dempster-Shafer (D-S) evidence theory has been used in many fields due to the flexibility and effectiveness in modeling uncertainties, which is the extension of classical probability. Recently, quantum probability which can express uncertainty has been used in many fields due to the existing of interference. Especially for human decision and cognition, interference can better model the process of decision. In order to better expand the applications of D-S evidence theory, the paper proposed quantum model of mass function which can consider the interference. In proposed quantum method, quantum mass function uses euler formula to represent. The paper also discusses some operations in quantum model of mass function. Moreover, the paper also discusses the relationship between quantum mass function and classical mass function by using some numerical examples. Classical mass function is the special case when there is no interference in quantum mass function.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
66
Top 1%
Top 10%
Top 1%
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