Roughness in n-ary hypergroups

This paper is aimed at studying the rough sets within the context of the commutative n-ary hypergroups. For this approach, the presentation of a detailed study of the various types of n-ary subhypergroups is of the greatest importance. This study was initiated in [V. Leoreanu-Fotea, Several types of n-ary subhypergroups, Italian Journal of Pure and Applied Mathematics, in press]. Subsequently, we shall use the findings in this study to define the upper and lower approximation of a subset with respect to an invertible n-ary subhypergroup of a commutative n-ary hypergroup. In the final part, the concept of n-ary rough subhypergroup is introduced.     

Fuzzy roughness of n-ary hypergroups based on a complete residuated lattice

This paper considers the relationships among L-fuzzy sets, rough sets and n-ary hypergroup theory. Based on a complete residuated lattice, the concept of (invertible) L-fuzzy n-ary subhypergroups of a commutative n-ary hypergroup is introduced and some related properties are presented. The notions of lower and upper L-fuzzy rough approximation operators with respect to an L-fuzzy n-ary subhypergroup are introduced and studied. Then, a new algebraic structure called (invertible) L-fuzzy rough n-ary subhypergroups is defined, and the (strong) homomorphism of lower and upper L-fuzzy rough approximation operators is studied.     

Soft rough fuzzy sets and soft fuzzy rough sets

Fuzzy set theory, soft set theory and rough set theory are mathematical tools for dealing with uncertainties and are closely related. Feng et al. introduced the notions of rough soft set, soft rough set and soft rough fuzzy set by combining fuzzy set, rough set and soft set all together. This paper is devoted to the further discussion of the combinations of fuzzy set, rough set and soft set. A new soft rough set model is proposed and its properties are derived. Furthermore, fuzzy soft set is employed to granulate the universe of discourse and a more general model called soft fuzzy rough set is established. The lower and upper approximation operators are presented and their related properties are surveyed.     

软模糊粗糙集

软集理论是1999年Molodtsov为了克服传统数学在处理不确定性问题时所遇到的困难而提出的一种新的数学工具。将软集理论与Z.Pawlak粗糙集结合起来,提出了软模糊粗糙集和软模糊粗糙群及它们的同态的概念,讨论了它们相关的性质。     

Interval-valued fuzzy n-ary subhypergroups of n-ary hypergroups

This paper provides a continuation of ideas presented by Davvaz and Corsini (J Intell Fuzzy Syst 18(4):377–382, 2007). Our aim in this paper is to introduce the concept of quasicoincidence of a fuzzy interval value with an interval-valued fuzzy set. This concept is a generalized concept of quasicoincidence of a fuzzy point within a fuzzy set. By using this new idea, we consider the interval-valued (∈, ∈ ∨q)-fuzzy n-ary subhypergroup of a n-ary hypergroup. This newly defined interval-valued (∈, ∈ ∨q)-fuzzy n-ary subhypergroup is a generalization of the usual fuzzy n-ary subhypergroup. Finally, we consider the concept of implication-based interval-valued fuzzy n-ary subhypergroup in an n-ary hypergroup; in particular, the implication operators in ￡ukasiewicz system of continuous-valued logic are discussed.     

变精度粗糙模糊集模型研究

介绍了Ziarko’s变精度粗糙集模型和粗糙模糊集模型,找出了它们的不足。基于支集相对错误分类率及误差参数β（0≤β<0.5）,提出了变精度粗糙模糊集模型,讨论了模型中β上、下近似算子的性质;分析了该模型与Ziarko’s变精度粗糙集模型和粗糙模糊集模型的关系;最后给出了该模型中近似约简的定义和方法,并通过实例分析说明了约简算法的有效性。     

几种粗糙集模型的推广研究

通过在经典粗糙集模型中引入函数,得到了一个广义的变精度粗糙集模型和一个广义的概率粗糙集模型。将这两个广义的模型进行比较研究,又得到了一个更广义的粗糙集模型,这个模型既是变精度粗糙集模型的推广也是概率粗糙集模型的推广,对推广模型的性质做了相应的研究。     

模糊集和粗糙集

介绍和比较了模糊集和粗糙集各自的理论和特点，同时讨论了两种理论相结合来研究问题的方法。     

A novel soft rough set: Soft rough hemirings and corresponding multicriteria group decision making

In this paper, we investigate the relationships among rough sets, soft sets and hemirings. The concept of soft rough hemirings is introduced, which is an extended notion of a rough hemiring. It is pointed out that in this paper, we first apply soft rough sets to algebraic structure-hemirings. Further, we first put forward the concepts of C-soft sets and CC-soft sets, which provide a new research idea for soft rough algebraic research. Moreover, we study roughness in hemirings with respect to MSR-approximation spaces. Some new soft rough operations over hemirings are explored. In particular, lower and upper MSR-hemirings (k-ideal and h-ideal) are investigated. Finally, we put forth an approach for multicriteria group decision making problem based on modified soft rough sets and offer an actual example.     

多粒度覆盖粗糙模糊集模型不确定性研究

针对覆盖粗糙模糊集中存在的上下近似不一致问题.引入一种更为合理的覆盖粗糙模糊集模型,讨论了该模型的结构与相关性质,定义了基于此模型的粗糙度度量方法.基于覆盖粗糙模糊集中粗糙度相等的情形,提出模糊集中极大模糊集的概念,并利用模糊集与极大模糊集的距离问题定义了模糊集的优劣次序,从而有效解决了模糊集在覆盖粗糙模糊集中粗糙度的度量问题.通过引入粗糙熵等相关概念,证明了此模型中仍然存在随最简覆盖变细,两种度量单调减少的规律,并通过实例进行了验证.从而为进一步揭示粗糙集、粗糙模糊集及覆盖粗糙模糊集之间的不确定性度量规律提供了理论依据.     

基于布尔矩阵的模糊粗糙集代数运算与表示定理

主要研究模糊粗糙集理论基本概念与基本运算的矩阵表示,用布尔矩阵对模糊粗糙集理论中的基本概念进行描述,并通过布尔矩阵运算性质研究、揭示和刻画模糊粗糙集知识空间的基本代数性质.文中定义了布尔矩阵"与积"和"或积"两种逻辑运算,分别对模糊粗糙集理论中的模糊可能(fuzzy diamond)算子和模糊必然(fuzzy box)算子计算过程进行描述,对模糊粗糙集理论的基本概念和基本代数性质给出了基于布尔矩阵的表示定理,为基于模糊粗糙集理论的知识表示与知识获取提供了一种能行与可计算的思路与方法.     

变精度粗糙集推广模型及其性质研究

数据挖掘的主要目标之一是进行有效分类,粗糙集的上下近似空间正是为了对信息系统进行分类。变精度粗糙集作为经典粗糙集的推广模型,目前研究仅局限于有限集。针对变精度粗糙集模型无法处理无限集合的问题,在变精度粗糙集和测度的理论基础上,提出了基于Lebesgue测度的变精度粗糙集模型。首先,引入Lebesgue测度的概念,构造了一种基于Lebesgue测度的变精度粗糙集模型,将变精度粗糙集理论推广到无限集;其次,定义了该模型的上、下近似空间;最后,证明了其相关性质。通过理论研究表明,该模型能有效处理无限集合问题,对变精度粗糙集的理论研究形成突破,也将极大的扩充其应用范围。     

基于随机抽样的模糊粗糙约简

传统的属性约简由于其时间复杂度和空间复杂度过高,几乎无法应用到大规模的数据集中.将随机抽样引入传统的模糊粗糙集中,使得属性约简的效率大幅度提升.首先,在统计下近似的基础上提出一种统计属性约简的定义.这里的约简不是原有意义上的约简,而是保持基于统计下近似定义的统计辨识度不变的属性子集.然后,采用抽样的方法计算统计辨识度的样本估计值,基于此估计值可以对统计属性重要性进行排序,从而可以设计一种快速的适用于大规模数据的序约简算法.由于随机抽样集以及统计近似概念的引入,该算法从时间和空间上均降低了约简的计算复杂度,同时又保持了数据集中信息含量几乎不变.最后,数值实验将基于随机抽样的序约简算法和两种传统的属性约简算法从以下3个方面进行了对比：计算属性约简时间消耗、计算属性约简空间消耗、约简效果.对比实验验证了基于随机抽样的序约简算法在时间与空间上的优势.     

一种基于组合变换的粗糙集属性约简算法

粗糙集的核心问题是知识的约简和获取。该文提出一种基于组合变换的粗糙集属性约简算法,此算法基于数理逻辑的组合推理,采用核属性和单属性进行过滤,并用超集关系对候选约简进行消减。分析表明,算法具有较好的时间复杂度和空间复杂度。     

基于包含度的直觉模糊粗糙集模型

将直觉模糊包含度引入粗糙集与直觉模糊集的融合建模。首先,介绍直觉模糊集在特殊格L上的定义;其次,将模糊包含度拓展到直觉模糊环境下,提出取值于特殊格L的直觉模糊包含度定义和计算公式;在此基础上,分别利用直觉模糊包含度的隶属度与非隶属度定义了上下近似算子,建立了直觉模糊相似关系下的直觉模糊粗糙集模型,并对近似算子的重要性质进行了证明。     

串行概率粗糙集近似

经典的概率粗糙集模型是基于等价关系和条件概率提出的。但在实际应用中,知识库存在多种不确定性因素,使得对象间的关系未必满足等价关系。因此在保证条件概率有意义的情况下,将等价关系推广到串行二元关系,讨论了串行关系下的概率粗糙集近似;研究了当目标概念发生变化时,串行概率粗糙下、上近似的性质;进一步,通过调整两个阈值,给出了对应的串行概率粗糙下、上近似的变化趋势。     

基于广义粗集覆盖约简的粗糙熵

在广义粗集覆盖约简理论中,由于集合的上下近似是由其覆盖约简来确定的,因此有必要寻求一种新的度量来刻画知识和粗集的粗糙性.通过引入信息熵以刻画广义粗集覆盖约简的知识粗糙性以及粗集粗糙性,提出了一种新的知识粗糙性和粗集粗糙性度量.得到知识粗糙熵和粗糙集的粗糙熵都随广义覆盖约简的变细而单调减少的结论,从信息论观点出发,对不完备信息系统粗集理论进行了探讨.     

变精度相容粗糙集模型

将变精度粗糙集的思想引入相容粗糙集,提出了两种变精度相容粗糙集模型,在模型I中,目标概念的下近似和边界域的交集非空;在模型II中,目标概念的下近似和边界域的交集为空。研究了两种模型中上、下近似算子的基本性质、两种模型之间的关系,以及与其他粗糙集模型之间的关系。     

粗糙集理论及其在智能系统中的应用

粗糙集理论是一种新型的处理含糊和不确定知识的数学工具，在智能系统中得到了广泛的应用，介绍了经典粗糙集理论的基本思想，上下近似集、属性约简和核等基本概念以及粗糙集的研究现状．介绍了粗糙集理论在智能系统中的应用，主要包括基于粗糙集理论的属性约简作为数据预处理的手段，基于粗糙集理论的相关性分析和基于粗糙集理论的系统建模和控制．指出了粗糙集理论在应用中遇到的问题和可能的研究方向。     

Roughness in Cayley graphs

In this paper, rough approximations of Cayley graphs are studied, and rough edge Cayley graphs are introduced. Furthermore, a new algebraic definition for pseudo-Cayley graphs containing Cayley graphs is proposed, and a rough approximation is expanded to pseudo-Cayley graphs. In addition, rough vertex pseudo-Cayley graphs and rough pseudo-Cayley graphs are introduced. Some theorems are provided from which properties such as connectivity and optimal connectivity are derived. This approach opens new research fields, such as data networks.