On the conjunction of possibility measures under intuitionistic evidence sets

Abstract There are many uncertain issues. To address these problems, many theories and models has been proposed. The conjunction of possibility measures can conjunct different possibility measures and deal with issues of decision making, which is an interesting model and has promising prospect. Mean...
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Abstract There are many uncertain issues. To address these problems, many theories and models has been proposed. The conjunction of possibility measures can conjunct different possibility measures and deal with issues of decision making, which is an interesting model and has promising prospect. Meanwhile, the intuitionistic evidence sets is based on the intuitionistic basic probability assignment, which means that the intuitionistic evidence sets can degenerate into classical basic probability assignment in some special cases. The intuitionistic evidence sets is more flexible and effective than the classical basic probability assignment. However, the conjunction of possibility measures has not been applied to intuitionistic evidence sets. So, what the conjunction of possibility measures to intuitionistic evidence sets is still an issue that need be discussed. To address this issue, this paper proposes the conjunction of possibility measures under intuitionistic evidence sets. Numerical comparison experiments are illustrated to prove the validity of the conjunction of possibility measures under intuitionistic evidence sets. The experimental results prove the proposed model can not only apply the conjunction of possibility measures to the intuitionistic evidence sets effectively, but also solve effectively the issues of decision making than other similar models.

Author:	Xue, Yige [VerfasserIn] Deng, Yong [VerfasserIn]

Format:	Electronic Article
Language:	English

Published:	2020

Subjects:	Intuitionistic basic probability assignment Possibility measures Intuitionistic evidence sets

Annotation:	© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Containing Work:	Enthalten in: Journal of ambient intelligence and humanized computing - Berlin : Springer, 2010, 12(2020), 7 vom: 10. Sept., Seite 7827-7836
Containing Work:	volume:12 ; year:2020 ; number:7 ; day:10 ; month:09 ; pages:7827-7836

Links:	Volltext [lizenzpflichtig]

DOI / URN:	10.1007/s12652-020-02508-8

Catalog id:	SPR044438958