An Evidence Fusion Method with Importance Discounting Factors based on Neutrosophic Probability Analysis in DSmT Framework

Autor: Qiang Guo, Haipeng Wang, You He, Yong Deng, Florentin Smarandache
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: Neutrosophic Sets and Systems, Vol 17, Pp 64-73 (2017)
Druh dokumentu: article
ISSN: 2331-6055
2331-608X
DOI: 10.5281/zenodo.1012213
Popis: To obtain effective fusion results of multi source evidences with different importance, an evidence fusion method with importance discounting factors based on neutrosopic probability analysis in DSmT framework is proposed. First, the reasonable evidence sources are selected out based on the statistical analysis of the pignistic probability functions of single focal elements. Secondly, the neutrosophic probability analysis is conducted based on the similarities of the pignistic probability functions from the prior evidence knowledge of the reasonable evidence sources. Thirdly, the importance discounting factors of the reasonable evidence sources are obtained based on the neutrosophic probability analysis and the reliability discounting factors of the real-time evidences are calculated based on probabilistic-based distances. Fourthly, the real-time evidences are discounted by the importance discounting factors and then the evidences with the mass assignments of neutrosophic empty sets are discounted by the reliability discounting factors.
Databáze: Directory of Open Access Journals