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      Decision making under measure-based granular uncertainty with intuitionistic fuzzy sets

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          Abstract

          Yager has proposed the decision making under measure-based granular uncertainty, which can make decision with the aid of Choquet integral, measure and representative payoffs. The decision making under measure-based granular uncertainty is an effective tool to deal with uncertain issues. The intuitionistic fuzzy environment is the more real environment. Since the decision making under measure-based granular uncertainty is not based on intuitionistic fuzzy environment, it cannot effectively solve the decision issues in the intuitionistic fuzzy environment. Then, when the issues of decision making are under intuitionistic fuzzy environment, what is the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets is still an open issue. To deal with this kind of issues, this paper proposes the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets. The decision making under measure-based granular uncertainty with intuitionistic fuzzy sets can effectively solve the decision making issues in the intuitionistic fuzzy environment, in other words, it can extend the decision making under measure-based granular uncertainty to the intuitionistic fuzzy environment. Numerical examples are applied to verify the validity of the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets. The experimental results demonstrate that the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets can represent the objects successfully and make decision effectively. In addition, a practical application of applied intelligence is used to compare the performance between the proposed model and the decision making under measure-based granular uncertainty. The experimental results show that the proposed model can solve some decision problems that the decision making under measure-based granular uncertainty cannot solve.

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          Most cited references 39

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          Intuitionistic fuzzy sets

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            Some geometric aggregation operators based on intuitionistic fuzzy sets

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              Is Open Access

              The Pseudo-Pascal Triangle of Maximum Deng Entropy

              PPascal triangle (known as Yang Hui Triangle in Chinese) is an important model in mathematics while the entropy has been heavily studied in physics or as uncertainty measure in information science. How to construct the the connection between Pascal triangle and uncertainty measure is an interesting topic. One of the most used entropy, Tasllis entropy, has been modelled with Pascal triangle. But the relationship of the other entropy functions with Pascal triangle is still an open issue. Dempster-Shafer evidence theory takes the advantage to deal with uncertainty than probability theory since the probability distribution is generalized as basic probability assignment, which is more efficient to model and handle uncertain information. Given a basic probability assignment, its corresponding uncertainty measure can be determined by Deng entropy, which is the generalization of Shannon entropy. In this paper, a Pseudo-Pascal triangle based the maximum Deng entropy is constructed. Similar to the Pascal triangle modelling of Tasllis entropy, this work provides the a possible way of Deng entropy in physics and information theory.
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                Author and article information

                Contributors
                dengentropy@uestc.edu.cn
                Journal
                Appl Intell
                Applied Intelligence
                Springer US (New York )
                0924-669X
                1573-7497
                5 February 2021
                : 1-10
                Affiliations
                [1 ]GRID grid.54549.39, ISNI 0000 0004 0369 4060, Institute of Fundamental and Frontier Science, , University of Electronic Science and Technology of China, ; Chengdu, 610054 China
                [2 ]GRID grid.412498.2, ISNI 0000 0004 1759 8395, School of Eduction, , Shaanxi Normal University, ; Xi’an, 710062 China
                Article
                2216
                10.1007/s10489-021-02216-6
                7862861
                © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 61973332
                Award Recipient :
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