Xue Yige, Deng Yong
Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, 610054 China.
School of Eduction, Shaanxi Normal University, Xi'an, 710062 China.
Appl Intell (Dordr). 2021;51(8):6224-6233. doi: 10.1007/s10489-021-02216-6. Epub 2021 Feb 5.
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.
亚格提出了基于测度的粒度不确定性下的决策方法，该方法可借助Choquet积分、测度和代表性收益进行决策。基于测度的粒度不确定性下的决策是处理不确定问题的有效工具。直觉模糊环境是更真实的环境。由于基于测度的粒度不确定性下的决策并非基于直觉模糊环境，所以它无法有效解决直觉模糊环境中的决策问题。那么，当决策问题处于直觉模糊环境时，基于直觉模糊集的基于测度的粒度不确定性下的决策仍是一个未解决的问题。为处理这类问题，本文提出了基于直觉模糊集的基于测度的粒度不确定性下的决策方法。基于直觉模糊集的基于测度的粒度不确定性下的决策能够有效解决直觉模糊环境中的决策问题，换句话说，它可以将基于测度的粒度不确定性下的决策扩展到直觉模糊环境。通过数值例子验证了基于直觉模糊集的基于测度的粒度不确定性下的决策的有效性。实验结果表明，基于直觉模糊集的基于测度的粒度不确定性下的决策能够成功地表示对象并有效地进行决策。此外，利用应用智能的实际应用来比较所提出的模型与基于测度的粒度不确定性下的决策之间的性能。实验结果表明，所提出的模型能够解决一些基于测度的粒度不确定性下的决策无法解决的决策问题。
