On the conjunction of possibility measures under intuitionistic evidence sets
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ORIGINAL RESEARCH
On the conjunction of possibility measures under intuitionistic evidence sets Yige Xue1 · Yong Deng1,2 Received: 4 April 2020 / Accepted: 27 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Intuitionistic basic probability assignment · Possibility measures · Intuitionistic evidence sets
1 Introduction In real life, there are a great quantity of unpredictabilities (Riesgo et al. 2018; Zhang et al. 2019; Jiang et al. 2019), which means there are a lot of questions about the unknown (Xiao 2020; Zhang et al. 2020; Mao et al. 2020). In order to dispose of these unpredictabilities, many mathematical models and algorithms are proposed, such as evidence theory (Gao and Deng 2020; Luo and Deng 2020; Wang et al. 2019), fuzzy sets (Zhang et al. 2018), intuitionistic fuzzy sets (IFS) (Song et al. 2019; Bouchet et al. 2020; Garg and Rani 2019), ordered weighted average operator Song and Deng (2019), Bayesian network (Jiang et al. 2019; Pan et al. 2019), possibility measure (Garg and Kumar 2020; Xue et al. 2018), Dempster-Shafer evidence theory (Dempster * Yong Deng [email protected]; [email protected] 1
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
School of Education, Shaanxi Normal University, Xi’an, Shaanxi 710062, China
2
2008; Yager 2019a), belief entropy (Gao and Deng 2020; Yan et al. 2020), D-number (Liu and Deng 2019), Z-number (Li et al. 2020; Kang et al. 2020; Liu et al. 2019) and belief structure (Yager 2018b)and so on. Mohammadzadeh et al. (2019) proposed a robust predictive synchronization u
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