共查询到20条相似文献,搜索用时 15 毫秒
1.
基于蕴涵的区间值直觉模糊粗糙集 总被引:3,自引:0,他引:3
提出一种基于区间值直觉模糊蕴涵的区间值直觉模糊粗糙集模型.首先,介绍了区间值直觉模糊集、区间值直觉模糊关系和区间值直觉模糊逻辑算子的概念;然后,利用区间值直觉模糊三角模和区间值直觉模糊蕴涵,在区间值直觉模糊近似空间中定义了区间值直觉模糊集的上近似和下近似;最后,给出并证明了这些近似算子的一些性质. 相似文献
2.
This paper presents a general framework for the study of relation-based (I,T)-intuitionistic fuzzy rough sets by using constructive and axiomatic approaches. In the constructive approach, by employing an intuitionistic fuzzy implicator I and an intuitionistic fuzzy triangle norm T, lower and upper approximations of intuitionistic fuzzy sets with respect to an intuitionistic fuzzy approximation space are first defined. Properties of (I,T)-intuitionistic fuzzy rough approximation operators are examined. The connections between special types of intuitionistic fuzzy relations and properties of intuitionistic fuzzy approximation operators are established. In the axiomatic approach, an operator-oriented characterization of (I,T)-intuitionistic fuzzy rough sets is proposed. Different axiom sets characterizing the essential properties of intuitionistic fuzzy approximation operators associated with various intuitionistic fuzzy relations are explored. 相似文献
3.
粗糙集理论和模糊集理论都是研究信息系统中知识的不完整、不确定性问题,把集对分析中的联系度概念应用于粗糙集中,说明了粗糙集联系度与下近似集和上近似集的值化的关系,将粗糙集联系度理论与模糊集理论相结合,提出了一种基于模糊集和粗糙集联系度的综合评价方法,实例验证了该方法对一大类复杂信息系统的知识发现具有一定的应用价值。 相似文献
4.
5.
6.
基于直觉模糊集和证据理论的群决策方法 总被引:1,自引:0,他引:1
针对属性值和权重均为直觉模糊数的多属性决策问题,提出一种基于直觉模糊集和证据理论的群决策方法.首先,对专家给出的每个方案的属性值和属性权重进行证据合成,在此基础上合成每个方案的所有属性值;然后,基于直觉模糊集相似度确定专家的相对权重,修正方案证据,并合成所有专家证据,得到方案的信任区间,根据信任区间的大小对方案进行排序;最后,通过数值案例验证了所提出方法的有效性和合理性. 相似文献
7.
8.
This paper presents a new extension of fuzzy sets: R-fuzzy sets. The membership of an element of a R-fuzzy set is represented as a rough set. This new extension facilitates the representation of an uncertain fuzzy membership with a rough approximation. Based on our definition of R-fuzzy sets and their operations, the relationships between R-fuzzy sets and other fuzzy sets are discussed and some examples are provided. 相似文献
9.
基于L.A.Zadeh模糊集的截集的概念给出了论域U上任意模糊子集的上、下近似的刻画,得到了基于模糊集的截集的粗糙集模型,亦即模糊粗糙集,实现了用论域U中的模糊集近似论域上的任意模糊集,进一步推广了Z.Pawlak粗糙集模型,扩展了粗糙集的应用范围。最后,研究了其基本性质以及其与其他粗糙集模型的关系。 相似文献
10.
11.
Dubois and Prade (1990) [1] introduced the notion of fuzzy rough sets as a fuzzy generalization of rough sets, which was originally proposed by Pawlak (1982) [8]. Later, Radzikowska and Kerre introduced the so-called (I,T)-fuzzy rough sets, where I is an implication and T is a triangular norm. In the present paper, by using a pair of implications (I,J), we define the so-called (I,J)-fuzzy rough sets, which generalize the concept of fuzzy rough sets in the sense of Radzikowska and Kerre, and that of Mi and Zhang. Basic properties of (I,J)-fuzzy rough sets are investigated in detail. 相似文献
12.
在经典的覆盖近似空间中,定义了区间直觉模糊概念的粗糙近似。通过区间直觉模糊覆盖概念,给出了一种基于区间直觉模糊覆盖的区间直觉模糊粗糙集模型。讨论了两种模型的一些相关性质。 相似文献
13.
14.
多准则决策分析(MCDA)用于解决分类、分级、选择、排序和描述问题,随着现实世界正变得由数据所驱动,传统的 MCDA 方法面临着更多的挑战.粗集方法是 MCDA 的有用工具,在多准则决策问题的分类框架下,从二元关系的角度对粗集方法的研究现状进行了评述,包括二元关系的建立、定义粗糙近似、导出决策规则和规则应用,并通过文献研究得出了基于粗集的 MCDA 方法的发展动态. 相似文献
15.
基于粗集理论的决策表知识获取方法研究 总被引:3,自引:3,他引:0
对决策表各条件分类和决策分类集合之间的关系进行了研究,提出了直接从各分类中计算决策表核及属性约简方法:依据属性约简,创建了一个多变量决策树;在此基础上,阐述了提取决策表中蕴含规则的方法,从而省去了在约简后的决策表中计算值约简步骤;通过实例,验证了这些方法的有效性。 相似文献
16.
粗糙集理论的概念性框架之一就是利用不可分辨关系和布尔推理作为数据约简和获取决策规则的基础.在分辨矩阵和决策矩阵概念的基础上,提出将约简分为4类,即信息表的对象约简、信息表的全局约简、决策表的对象约简和决策表的全局约简,其中决策表的对象约简对应决策规则.从模式的角度对约简和决策规则进行了分析,利用决策矩阵和决策函数,给出了获取最小决策规则的一种算法,上述结论可以作为设计启发式算法的基础,并用例子对结论进行了说明. 相似文献
17.
18.
The concept of the rough set was originally proposed by Pawlak as a formal tool for modelling and processing incomplete information in information systems, then in 1990, Dubois and Prade first introduced the rough fuzzy sets and fuzzy rough sets as a fuzzy extension of the rough sets. The aim of this paper is to present a new extension of the rough set theory by means of integrating the classical Pawlak rough set theory with the interval-valued fuzzy set theory, i.e., the interval-valued fuzzy rough set model is presented based on the interval-valued fuzzy information systems which is defined in this paper by a binary interval-valued fuzzy relations RF(i)(U×U) on the universe U. Several properties of the rough set model are given, and the relationships of this model and the others rough set models are also examined. Furthermore, we also discuss the knowledge reduction of the classical Pawlak information systems and the interval-valued fuzzy information systems respectively. Finally, the knowledge reduction theorems of the interval-valued fuzzy information systems are built. 相似文献
19.
为准确及时地发现高速公路上的事故隐患,有效地减少交通延误,保障道路安全,提出了一种新的基于模糊C均值(FCM)聚类和模糊粗糙集的交通事件自动检测模型。模型分为离散化、推理规则建立和模糊推理三个步骤。在属性离散化时,提出用常用的隶属度函数来拟合FCM聚类后的结果,并用此函数和参数来实现属性数据的离散化,避免了每次输入数据都必须通过聚类操作来进行离散化;采用了粗糙集理论建立推理规则,选择和交通事件密切相关属性并进行规则的约简,加速了模糊推理的速度;最后采用Max-Min模糊推理方法对交通事件进行检测。通过多种检测方法对比测试,结果表明了此模型在总体性能上优于传统的检测方法,验证了此模型的有效性,为交通事件的检测提供了一种新的思路。 相似文献
20.
The integration of fuzzy sets and rough sets can lead to a hybrid soft-computing technique which has been applied successfully to many fields such as machine learning, pattern recognition and image processing. The key to this soft-computing technique is how to set up and make use of the fuzzy attribute reduct in fuzzy rough set theory. Given a fuzzy information system, we may find many fuzzy attribute reducts and each of them can have different contributions to decision-making. If only one of the fuzzy attribute reducts, which may be the most important one, is selected to induce decision rules, some useful information hidden in the other reducts for the decision-making will be losing unavoidably. To sufficiently make use of the information provided by every individual fuzzy attribute reduct in a fuzzy information system, this paper presents a novel induction of multiple fuzzy decision trees based on rough set technique. The induction consists of three stages. First several fuzzy attribute reducts are found by a similarity based approach, and then a fuzzy decision tree for each fuzzy attribute reduct is generated according to the fuzzy ID3 algorithm. The fuzzy integral is finally considered as a fusion tool to integrate the generated decision trees, which combines together all outputs of the multiple fuzzy decision trees and forms the final decision result. An illustration is given to show the proposed fusion scheme. A numerical experiment on real data indicates that the proposed multiple tree induction is superior to the single tree induction based on the individual reduct or on the entire feature set for learning problems with many attributes. 相似文献