• 人工智能 •

### 基于改进模糊熵和证据推理的多属性决策方法

1. 福州大学 经济与管理学院, 福州 350116
• 收稿日期:2018-04-03 修回日期:2018-05-05 出版日期:2018-10-10 发布日期:2018-10-13
• 通讯作者: 熊宁欣
• 作者简介:熊宁欣(1994-),女,福建三明人,硕士研究生,主要研究方向:决策理论与方法、证据推理;王应明(1964-),男,江苏海安人,教授,博士,主要研究方向:决策理论与方法、数据包络分析。
• 基金资助:
国家自然科学基金资助项目（61773123）。

### Multiple attribute decision method based on improved fuzzy entropy and evidential reasoning

1. School of Economics & Management, Fuzhou University, Fuzhou Fujian 350116, China
• Received:2018-04-03 Revised:2018-05-05 Online:2018-10-10 Published:2018-10-13
• Supported by:
This work is partially supported by the National Natural Science Foundation of China (61773123).

Abstract: Aiming at the problem that attribute weights are difficult to obtain under the framework of evidential reasoning, a multi-attribute decision method based on improved fuzzy entropy and evidential reasoning was proposed. Firstly, the formula of trigonometric fuzzy entropy under the framework of belief decision matrix of evidential reasoning was defined, and it was proved that it satisfies the four axiomatic definitions of entropy. Secondly, the proposed method can simultaneously deal with two situations in which the attribute weights are completely unknown and the attribute weights information are partially known. When the attribute weights were completely unknown, the attribute weights were calculated based on the basic idea of fuzzy entropy and entropy weight method under the framework of belief. When part of the information of attribute weights were known, the weighted fuzzy entropy was defined, and the linear programming model with the minimum expected fuzzy entropy was established to get the optimal attribute weights. Finally, the evidential reasoning algorithm was used to aggregate the degree of belief of attributes and got the result of the ranking of alternatives combined with expected utility theory. Through example calculations and comparative analysis with the method of traditional fuzzy entropy, it is verified that the proposed method can more fully reflect the original decision information, which is more objective and general.