粗代数研究

在粗糙集的代数方法研究中,一个重要的方面是从粗糙集的偶序对((下近似集,上近似集()表示入手,通过定义偶序对的基本运算,从而构造出相应粗代数,并寻找能够抽象刻画偶序对性质的一般代数结构.其中最有影响的粗代数分别是粗双Stone代数、粗Nelson代数和近似空间代数,它们对应的一般代数结构分别是正则双Stone代数、半简单Nelson代数和预粗代数.通过建立这些粗代数中算子之间的联系,证明了:(a) 近似空间代数可转化为半简单Nelson代数和正则双Stone代数;(b) 粗Nelson代数可转化为预粗代数和正则双Stone代数;(c) 粗双Stone代数可化为预粗代数和半简单Nelson代数,从而将3个不同角度的研究统一了起来.     

Algebraic Structures of Rough Sets in Representative Approximation Spaces

In this paper a generalized notion of an approximation space is considered. By an approximation space we mean an ordered pair (U, ), where U is a finite nonempty set and is a covering of U. According to connections between rough sets and concepts we define two types of approximation operations. Hence we obtain two families of rough sets. We show that these families form lattices in special types of representative approximation spaces. The operations on rough sets defined in the above lattices are analogous to classical operations on sets.     

粗糙蕴涵

Rough implication operator is the emphasis and difficulty in the study of rough logic. Due to the shortage of rough implication in [3]～[5], we redefine rough set and rough implication operator by Stone algebra, and introduce new rough operators such as rough intersection, rough union, and rough complement. Moreover the characteristics of the proposed rough implication are investigated ,and we also point out that the proposed implication operation is superior to that of three-valued Lukasiewicz logic.     

基于Lukasiewicz三角模及其剩余蕴涵的模糊粗糙集

讨论基于Lukasiewicz三角模及其剩余蕴涵的模糊粗糙集模型,研究了相应模糊粗糙集的代数性质,证明了自反模糊关系下该模型中的下近似集构成一个模糊拓扑,且上、下近似算子恰为其闭包及内部算子。     

Rough set theory for the interval-valued fuzzy information systems

The notion of a rough set was originally proposed by Pawlak [Z. Pawlak, Rough sets, International Journal of Computer and Information Sciences 11 (5) (1982) 341-356]. Later on, Dubois and Prade [D. Dubois, H. Prade, Rough fuzzy sets and fuzzy rough sets, International Journal of General System 17 (2-3) (1990) 191-209] introduced rough fuzzy sets and fuzzy rough sets as a generalization of rough sets. This paper deals with an interval-valued fuzzy information system by means of integrating the classical Pawlak rough set theory with the interval-valued fuzzy set theory and discusses the basic rough set theory for the interval-valued fuzzy information systems. In this paper we firstly define the rough approximation of an interval-valued fuzzy set on the universe U in the classical Pawlak approximation space and the generalized approximation space respectively, i.e., the space on which the interval-valued rough fuzzy set model is built. Secondly several interesting properties of the approximation operators are examined, and the interrelationships of the interval-valued rough fuzzy set models in the classical Pawlak approximation space and the generalized approximation space are investigated. Thirdly we discuss the attribute reduction of the interval-valued fuzzy information systems. Finally, the methods of the knowledge discovery for the interval-valued fuzzy information systems are presented with an example.     

Characterization of rough set approximations in Atanassov intuitionistic fuzzy set theory

The primitive notions in rough set theory are lower and upper approximation operators defined by a fixed binary relation and satisfying many interesting properties. Many types of generalized rough set models have been proposed in the literature. This paper discusses the rough approximations of Atanassov intuitionistic fuzzy sets in crisp and fuzzy approximation spaces in which both constructive and axiomatic approaches are used. In the constructive approach, concepts of rough intuitionistic fuzzy sets and intuitionistic fuzzy rough sets are defined, properties of rough intuitionistic fuzzy approximation operators and intuitionistic fuzzy rough approximation operators are examined. Different classes of rough intuitionistic fuzzy set algebras and intuitionistic fuzzy rough set algebras are obtained from different types of fuzzy relations. In the axiomatic approach, an operator-oriented characterization of rough sets is proposed, that is, rough intuitionistic fuzzy approximation operators and intuitionistic fuzzy rough approximation operators are defined by axioms. Different axiom sets of upper and lower intuitionistic fuzzy set-theoretic operators guarantee the existence of different types of crisp/fuzzy relations which produce the same operators.     

An axiomatic characterization of a fuzzy generalization of rough sets

In rough set theory, the lower and upper approximation operators defined by a fixed binary relation satisfy many interesting properties. Several authors have proposed various fuzzy generalizations of rough approximations. In this paper, we introduce the definitions for generalized fuzzy lower and upper approximation operators determined by a residual implication. Then we find the assumptions which permit a given fuzzy set-theoretic operator to represent a upper (or lower) approximation derived from a special fuzzy relation. Different classes of fuzzy rough set algebras are obtained from different types of fuzzy relations. And different sets of axioms of fuzzy set-theoretic operator guarantee the existence of different types of fuzzy relations which produce the same operator. Finally, we study the composition of two approximation spaces. It is proved that the approximation operators in the composition space are just the composition of the approximation operators in the two fuzzy approximation spaces.     

粗集中上下近似运算的逻辑性质

1 引论近年来,粗集理论的实际应用与理论探讨已成为计算机科学中的一个热点问题。1995年Pawlak曾在文[6]中指出,粗集的逻辑性质研究将是今后粗集理论的一个重要同题。本文正是通过深入研究拓扑布尔代数与粗集的关系,给出了关于有限拓扑布尔代数的表示定理,从逻辑上全面刻画了粗集中上下近似运算这一核心概念。     

On intuitionistic fuzzy rough sets and their topological structures

In this paper, lower and upper approximations of intuitionistic fuzzy sets with respect to an intuitionistic fuzzy approximation space are first defined. Properties of intuitionistic fuzzy approximation operators are examined. Relationships between intuitionistic fuzzy rough set approximations and intuitionistic fuzzy topologies are then discussed. It is proved that the set of all lower approximation sets based on an intuitionistic fuzzy reflexive and transitive approximation space forms an intuitionistic fuzzy topology; and conversely, for an intuitionistic fuzzy rough topological space, there exists an intuitionistic fuzzy reflexive and transitive approximation space such that the topology in the intuitionistic fuzzy rough topological space is just the set of all lower approximation sets in the intuitionistic fuzzy reflexive and transitive approximation space. That is to say, there exists an one-to-one correspondence between the set of all intuitionistic fuzzy reflexive and transitive approximation spaces and the set of all intuitionistic fuzzy rough topological spaces. Finally, intuitionistic fuzzy pseudo-closure operators in the framework of intuitionistic fuzzy rough approximations are investigated.     

Rough implication operator based on strong topological rough algebras

The role of topological De Morgan algebra in the theory of rough sets is investigated. The rough implication operator is introduced in strong topological rough algebra that is a generalization of classical rough algebra and a topological De Morgan algebra. Several related issues are discussed. First, the two application directions of topological De Morgan algebras in rough set theory are described, a uniform algebraic depiction of various rough set models are given. Secondly, based on interior and closure operators of a strong topological rough algebra, an implication operator (called rough implication) is introduced, and its important properties are proved. Thirdly, a rough set interpretation of classical logic is analyzed, and a new semantic interpretation of ?ukasiewicz continuous-valued logic system ?uk is constructed based on rough implication. Finally, strong topological rough implication algebra (STRI-algebra for short) is introduced. The connections among STRI-algebras, regular double Stone algebras and RSL-algebras are established, and the completeness theorem of rough logic system RSL is discussed based on STRI-algebras.     

Soft sets combined with fuzzy sets and rough sets: a tentative approach

Theories of fuzzy sets and rough sets are powerful mathematical tools for modelling various types of uncertainty. Dubois and Prade investigated the problem of combining fuzzy sets with rough sets. Soft set theory was proposed by Molodtsov as a general framework for reasoning about vague concepts. The present paper is devoted to a possible fusion of these distinct but closely related soft computing approaches. Based on a Pawlak approximation space, the approximation of a soft set is proposed to obtain a hybrid model called rough soft sets. Alternatively, a soft set instead of an equivalence relation can be used to granulate the universe. This leads to a deviation of Pawlak approximation space called a soft approximation space, in which soft rough approximations and soft rough sets can be introduced accordingly. Furthermore, we also consider approximation of a fuzzy set in a soft approximation space, and initiate a concept called soft–rough fuzzy sets, which extends Dubois and Prade’s rough fuzzy sets. Further research will be needed to establish whether the notions put forth in this paper may lead to a fruitful theory.     

Fuzzy rough set on probabilistic approximation space over two universes and its application to emergency decision‐making

Probabilistic approaches to rough sets are still an important issue in rough set theory. Although many studies have been written on this topic, they focus on approximating a crisp concept in the universe of discourse, with less effort on approximating a fuzzy concept in the universe of discourse. This article investigates the rough approximation of a fuzzy concept on a probabilistic approximation space over two universes. We first present the definition of a lower and upper approximation of a fuzzy set with respect to a probabilistic approximation space over two universes by defining the conditional probability of a fuzzy event. That is, we define the rough fuzzy set on a probabilistic approximation space over two universes. We then define the fuzzy probabilistic approximation over two universes by introducing a probability measure to the approximation space over two universes. Then, we establish the fuzzy rough set model on the probabilistic approximation space over two universes. Meanwhile, we study some properties of both rough fuzzy sets and fuzzy rough sets on the probabilistic approximation space over two universes. Also, we compare the proposed model with the existing models to show the superiority of the model given in this paper. Furthermore, we apply the fuzzy rough set on the probabilistic approximation over two universes to emergency decision‐making in unconventional emergency management. We establish an approach to online emergency decision‐making by using the fuzzy rough set model on the probabilistic approximation over two universes. Finally, we apply our approach to a numerical example of emergency decision‐making in order to illustrate the validity of the proposed method.     

多粒化的粗糙集代数

众所周知,一个粗糙集代数是由一个集合代数加上一对近似算子构成的。一方面 ,在公理化的方法下对经典的多粒化粗糙集代数系统进行了讨论,可知经典的粗糙集代数没有很好的性质;另一方面,给出了单调等价关系的定义,并给出了基于单调等价关系的多粒化近似算子的概念,在此基础上讨论了粗糙集代数的性质,并得到了诸多结果。     

多粒化的模糊粗糙集代数

众所周知,一个粗糙集代数是由一个集合代数加上一对近似算子构成的。首先利用公理化的方法探讨经典的多粒化模糊粗糙集代数系统,可知经典的多粒化模糊粗糙集代数没有很好的性质;其次,引入 具有最小(大)元的等价关系的定义,并给出了基于具有最小(大)元等价关系的多粒化模糊近似算子的概念,在此基础上讨论了模糊粗糙集代数的性质,并得到了诸多结果。     

On intuitionistic fuzzy topologies based on intuitionistic fuzzy reflexive and transitive relations

Topologies and rough set theory are widely used in the research field of machine learning and cybernetics. An intuitionistic fuzzy rough set, which is the result of approximation of an intuitionistic fuzzy set with respect to an intuitionistic fuzzy approximation space, is an extension of fuzzy rough sets. For further studying the theories and applications of intuitionistic fuzzy rough sets, in this paper, we investigate the topological structures of intuitionistic fuzzy rough sets. We show that an intuitionistic fuzzy rough approximation space can induce an intuitionistic fuzzy topological space in the sense of Lowen if and only if the intuitionistic fuzzy relation in the approximation space is reflexive and transitive. We also examine the sufficient and necessary conditions that an intuitionistic fuzzy topological space can be associated with an intuitionistic fuzzy reflexive and transitive relation such that the induced lower and upper intuitionistic fuzzy rough approximation operators are, respectively, the intuitionistic fuzzy interior and closure operators of the given topology.     

粗糙模糊集的构造与公理化方法

用构造性方法和公理化研究了粗糙模糊集．由一个一般的二元经典关系出发构造性地定义了一对对偶的粗糙模糊近似算子，讨论了粗糙模糊近似算子的性质，并且由各种类型的二元关系通过构造得到了各种类型的粗糙模糊集代数．在公理化方法中，用公理形式定义了粗糙模糊近似算子，各种类型的粗糙模糊集代数可以被各种不同的公理集所刻画．阐明了近似算子的公理集可以保证找到相应的二元经典关系，使得由关系通过构造性方法定义的粗糙模糊近似算子恰好就是用公理化定义的近似算子。     

£ukasiewicz三值命题逻辑系统中公式的概率真度理论

利用势为3的非均匀概率空间的无穷乘积,在£ukasiewicz三值命题逻辑中引入了公式的概率真度概念,证明了全体公式的概率真度值之集在[0,1]中没有孤立点;利用概率真度定义了概率相似度和伪距离,进而建立了概率逻辑度量空间,证明了该空间中没有孤立点,为三值命题的近似推理理论提供了一种可能的框架。     

Mitter conjecture for low dimensional estimation algebras in non-linear filtering†

It is well known that a systematical way to construct finite dimensional filter is to classify all finite dimensional estimation algebras. Mitter conjecture, which states that all functions in any finite dimensional estimation algebra are necessarily degree one polynomial, plays a crucial role in classifying all finite dimensional estimation algebras. The purpose of this paper is to prove the Mitter Conjecture for estimation algebra of dimension at most 5 with arbitrary state space dimension.     

一种覆盖粗糙模糊集模型

粗糙集扩展模型的研究是粗糙集理论研究的一个重要问题.其中,基于覆盖的粗糙集模型扩展是粗糙集扩展模型中的重要一类.覆盖近似空间中的概念近似是从覆盖近似空间中获取知识的关键.目前,研究者对覆盖近似空间中经典集合的近似进行了较多的研究.针对覆盖近似空间中模糊集合的近似,虽然不同的覆盖粗糙模糊集模型被提了出来,但它们都存在不合理性.从规则的置信度出发,提出了一种新的覆盖粗糙模糊集模型.该模型修正了已有模型中存在对象在下近似中不确定可分和上近似中不近似可分的问题.分析了具有偏序关系的两个覆盖近似空间中上、下近似之间的关系,发现两个不同覆盖生成相同覆盖粗糙模糊集的充要条件是这两个覆盖的约简恒等.分析了新模型与Wei模型、Xu模型之间的关系,发现这两种模型是新模型的两种极端情况,且其应用前提是覆盖为一元覆盖.这些结论将为覆盖粗糙模糊集模型应用于决策为模糊的情形提供理论基础.     

The further investigation of covering-based rough sets: Uncertainty characterization, similarity measure and generalized models

The notion of rough sets was originally proposed by Pawlak. In Pawlak’s rough set theory, the equivalence relation or partition plays an important role. However, the equivalence relation or partition is restrictive for many applications because it can only deal with complete information systems. This limits the theory’s application to a certain extent. Therefore covering-based rough sets are derived by replacing the partitions of a universe with its coverings. This paper focuses on the further investigation of covering-based rough sets. Firstly, we discuss the uncertainty of covering in the covering approximation space, and show that it can be characterized by rough entropy and the granulation of covering. Secondly, since it is necessary to measure the similarity between covering rough sets in practical applications such as pattern recognition, image processing and fuzzy reasoning, we present an approach which measures these similarities using a triangular norm. We show that in a covering approximation space, a triangular norm can induce an inclusion degree, and that the similarity measure between covering rough sets can be given according to this triangular norm and inclusion degree. Thirdly, two generalized covering-based rough set models are proposed, and we employ practical examples to illustrate their applications. Finally, relationships between the proposed covering-based rough set models and the existing rough set models are also made.