越览（94）——精读硕士论文的摘要

摘要：This issue of tweets will introduce the abstract of the master's thesis "Research on Multi-Attribute Fuzzy Decision-making Method

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“越览（94）——精读硕士论文

《基于不完备信息系统的多属性模糊决策方法研究》

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"Yuelan (94): Intensive reading of the master's thesis

"Abstractof multi-attribute fuzzy decision-making

methods based on incomplete information systems".

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一、内容摘要（Summary of content）

本期推文将从思维导图、精读内容、知识补充三个方面介绍硕士论文《基于不完备信息系统的多属性模糊决策方法研究》的摘要。

This issue of tweets will introduce the abstract of the master's thesis "Research on Multi-Attribute Fuzzy Decision-making Method Based on Incomplete Information System" from three aspects: mind map, intensive reading content, and knowledge supplement.

二、思维导图（Mind mapping）

三、精读内容（Intensive reading content）

（一）研究背景及目标（Research background and objectives）

本篇博士论文的摘要，首先介绍了论文的研究背景：传统不完备信息系统决策依赖粗糙集理论，但难以有效解决多属性决策的选择与排序问题，尤其在信息缺失情况下，传统方法更显局限。然后，介绍了本文的研究目标：融合信息系统与模糊模式识别，引入前景理论，提出针对完备与不完备信息系统的三种多属性模糊决策方法，解决多属性决策问题。

The abstract of this doctoral dissertation first introduces the research background of the paper: traditional incomplete information system decision-making relies on rough set theory, but it is difficult to effectively solve the selection and sorting problems of multi-attribute decision-making, especially in the case of missing information, the traditional method is more limited. Then, the research objectives of this paper are introduced: integrating information systems and fuzzy pattern recognition, introducing prospect theory, and proposing three multi-attribute fuzzy decision-making methods for complete and incomplete information systems to solve multi-attribute decision-making problems.

（二）研究内容（Research content）

在介绍完本文的研究背景及目标之后，摘要中的第三、四、五段分别对应了论文第三、四、五章的内容，介绍了每一章节的研究内容。

After introducing the research background and objectives of this paper, the third, fourth and fifth paragraphs in the abstract correspond to the contents of the third, fourth and fifth chapters of the paper respectively, introducing the research content of each chapter.

本文首先构建了混合信息系统模糊多属性决策模型，利用隶属度函数集提出新型混合完备信息系统，确定属性权重并构建标准集与方案集，增强决策者影响力。相比传统TOPSIS方法，模型简化了流程和计算复杂度，并通过“公司招聘”实例验证了其可行性和优越性。

This paper first constructs a fuzzy multi-attribute decision-making model for a hybrid information system, proposes a new hybrid complete information system using a membership function set, determines attribute weights and constructs a standard set and a solution set to enhance the influence of decision makers. Compared with the traditional TOPSIS method, the model simplifies the process and computational complexity, and verifies its feasibility and superiority through a "company recruitment" example.

接着，本文提出基于属性重要度和信息完备度的不完备信息系统决策模型，通过隶属度函数构建新型系统，利用信息补足法完善数据，确定权重与层级，计算方案贴近度选择最优方案，并通过“优秀教师”实例验证有效性。

Next, this paper proposes a decision-making model for incomplete information systems based on attribute importance and information completeness, constructs a new system through membership function, uses information complementation method to improve data, determines weights and levels, calculates the closeness of solutions to select the optimal solution, and verifies its effectiveness through the "excellent teacher" example.

最后提出基于前景理论的不完备信息模糊模式决策方法，将决策者偏好纳入权重计算，更符合现代决策需求。通过信息补足法确定标准集，结合容差关系与属性权重构建方案集，利用价值函数计算综合前景收益与损失值。以“优秀教师”实例验证方法有效性，并与传统方法对比，突出决策者偏好的影响和方法优越性。

Finally, an incomplete information fuzzy model decision method based on prospect theory is proposed, which incorporates decision maker preferences into weight calculation, which is more in line with modern decision-making needs. The standard set is determined by the information complement method, the solution set is constructed by combining the tolerance relationship and attribute weights, and the comprehensive prospect benefit and loss value is calculated using the value function. The effectiveness of the method is verified by the "excellent teacher" example, and compared with the traditional method, highlighting the influence of decision maker preferences and the superiority of the method.

四、知识补充（Knowledge supplement）

隶属度函数集是模糊数学中的概念，用于描述某个元素属于某模糊集合的程度。它是一组隶属度函数的集合，每个隶属度函数对应一个属性或变量，用来量化模糊集合中的元素与该属性的关联程度。

Membership function set is a concept in fuzzy mathematics, which is used to describe the degree to which an element belongs to a fuzzy set. It is a set of membership functions, each of which corresponds to an attribute or variable, and is used to quantify the degree of association between the elements in the fuzzy set and the attribute.

（一）核心特点（Core features）

隶属度函数：每个函数定义在一个具体属性或变量的定义域上，其值范围在0，1之间。0表示完全不属于该模糊集合。1表示完全属于该模糊集合。中间值表示部分隶属程度。

Membership function: Each function is defined on the domain of a specific attribute or variable, and its value range is between 0 and 1. 0 means that it does not belong to the fuzzy set at all. 1 means that it completely belongs to the fuzzy set. Intermediate values represent partial membership.

隶属度函数集：是多个隶属度函数的集合，用于处理多属性或多变量问题。

Membership function set: It is a collection of multiple membership functions, used to deal with multi-attribute or multi-variable problems.

（二）作用（Effect）

在模糊决策和模式识别中，隶属度函数集用来对不同属性或条件进行模糊建模，帮助描述和处理复杂、不确定的信息。

In fuzzy decision making and pattern recognition, membership function sets are used to perform fuzzy modeling on different attributes or conditions, helping to describe and process complex and uncertain information.

在决策系统中，它可以衡量方案或对象与不同标准的符合程度，为决策提供量化依据。

In the decision-making system, it can measure the degree of conformity of plans or objects with different standards and provide a quantitative basis for decision-making.

（三）应用示例（Application examples）

在多属性决策中：隶属度函数集为每个属性（如成本、时间、质量）定义一个隶属度函数，用于评估每个方案在该属性下的表现。

In multi-attribute decision making: The membership function set defines a membership function for each attribute (such as cost, time, quality) to evaluate the performance of each option under that attribute.

在不完备信息系统中：通过隶属度函数集处理缺失数据或模糊信息，以实现更精确的决策或分类。

In incomplete information systems: missing data or fuzzy information can be processed through a set of membership functions to achieve more accurate decisions or classifications.

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