模糊关系传递性有关指标之间的关系

研究模糊关系与传递性有关的指标,这些指标包括T-传递指标、S-负传递指标、T-S-半传递指标、T-S-Ferrers关系指标等,详细讨论了它们之间的关系,从而推广了Fodor和王有关传递性性质之间的相关结果.     

模糊关系的迹与其性质指标之间的关系

讨论用模糊关系的迹去刻画模糊关系的各种性质指标,从而揭示模糊关系的迹与一些性质指标如自反、T-非对称、S-强完全、T-传递、S-负传递指标之间的联系.通过我们的讨论,推广了文献中迹与模糊关系性质联系的相关结果.     

模糊关系性质的模糊T-近似算子刻画

在定义一般模糊关系下的模糊T-近似的基础上，用模糊T-近似算子来刻画模糊关系的自反性、对称性、T-传递性等性质，并讨论了模糊T-粗糙集的一些性质。     

模糊选择函数T-拟传递合理性充要条件的研究

T-拟传递合理性对模糊选择函数的研究至关重要.基于选择集为正规模糊选择集,对任意的t-模,利用文献中已有的结论,深入研究了模糊选择函数T-拟传递合理性的刻画问题,得到了模糊选择函数T-拟传递合理性的充要条件.     

具有t-范数的模糊M-半群的性质

本文利用范数,引入M-半群的T-模糊M-子半群的概念,刻划其性质和特征。进一步,我们给出T-模糊M-子半群R与T-模糊M-子半群S的直积是T-模糊M-子半群这一结论。同时,我们证明了T-模糊M-子半群可分解成T-模糊M-子半群的直积。     

模糊关系的非循环度量

本文定义并研究了一般t-模下模糊关系的非循环指标。首先,在所涉及的t-模T左连续的情况下,讨论了T-非循环指标与T-非对称指标以及T-传递闭包的非自反指标的关系;其次,研究了模糊关系交的T-非循环指标;最后,在取小t-模下,研究了T-非循环指标与T-传递指标以及非自反指标的关系。从而将文献中的一些非循环性质推广为程度描述.     

模糊全序及偏序结构

以Fodor等人对模糊偏好构模的公理化方法的一个特例为基础,给出了模糊全序以及偏序结构的定义,并详细讨论了两个结构的性质。     

T-染色和G_n~d图的T-边柞(Edge Span)

给定一个包含0的有限正整数集T,一个简单图G的一个T-染色是定义在G的顶点集V(G)上的一个非负函数f,满足对任意的uv∈E(G)有|f(u)-f(v)| T.一个T-染色f的边柞(edgespan)定义为最大的|f(x)-f(y)|,xy∈E(G),一个图G的边柞(edgespan)是G的所有T-染色中最小的边柞(edgespan).这篇文章研究了当T={0,1,2,…,k-1}时,Gdn图的T-边柞(edgespan),找到了当n≡1(modd)时Gdn图的T-边柞(edgespan)的确切值,和其他情况下的上下界.     

Morita Context环的T-幂零性和周期性

本文研究了Morita Context环C的T-幂零性、T-幂等性、T-稳定性和周期性、弱周期性、广义周期性、拟周期性.利用经典环论方法,获得了C的上述性质与C中A, B, V, W的性质之间的关系.     

T-染色和Gdn图的T-边柞(Edge Span)

给定一个包含0的有限正整数集T,一个简单图G的一个T-染色是定义在G的顶点集V(G)上的一个非负函数f,满足对任意的uv∈E(G)有|f(u)-f(v)| T.一个T-染色f的边柞(edge span)定义为最大的|f(x)-f(y)|,xy∈E(G),一个图G的边柞(edge span)是G的所有T-染色中最小的边柞(edge span).这篇文章研究了当T={0,1,2,…,k-1}时,Gdn图的T-边柞(edge span),找到了当n≡1(mod d)时Gdn图的T-边柞(edge span)的确切值,和其他情况下的上下界.     

模糊全半序结构

以Fodor公理为基础，给出一类重要的模糊偏好结构——模糊全半序结构的一个较为一般的定义，同时对其性质进行讨论。     

Minimizing a Linear Objective Function with Fuzzy Relation Equation Constraints

An minimization problem with a linear objective function subject to fuzzy relation equations using max-product composition has been considered by Loetamonphong and Fang. They first reduced the problem by exploring the special structure of the problem and then proposed a branch-and-bound method to solve this 0-1 integer programming problem. In this paper, we provide a necessary condition for an optimal solution of the minimization problems in terms of one maximum solution derived from the fuzzy relation equations. This necessary condition enables us to derive efficient procedures for solving such optimization problems. Numerical examples are provided to illustrate our procedures.     

模糊粗糙近似算子的拓扑性质

考虑论域上一二元关系所决定的模糊粗糙近似算子的拓扑性质,证明了任一自反二元关系可以决定一模糊拓扑.并且,当二元关系自反对称时,该模糊拓扑中的元是开集当且仅当它是闭集;当二元关系自反传递时,该模糊拓扑的闭包与内部算子恰为模糊粗糙上、下近似算子.     

A Note on Fuzzy Relation Programming Problems with Max-Strict-t-Norm Composition

The fuzzy relation programming problem is a minimization problem with a linear objective function subject to fuzzy relation equations using certain algebraic compositions. Previously, Guu and Wu considered a fuzzy relation programming problem with max-product composition and provided a necessary condition for an optimal solution in terms of the maximum solution derived from the fuzzy relation equations. To be more precise, for an optimal solution, each of its components is either 0 or the corresponding component's value of the maximum solution. In this paper, we extend this useful property for fuzzy relation programming problem with max-strict-t-norm composition and present it as a supplemental note of our previous work.     

Solving nonlinear multi-objective optimization problems with fuzzy relation inequality constraints regarding Archimedean triangular norm compositions

We propose an approach to solve a nonlinear multi-objective problem subject to fuzzy relation inequalities with max-Archimedean-t-norm composition by a genetic algorithm. The additive generator of Archimedean t-norms is utilized to reform the existent genetic algorithm to solve the constrained nonlinear multi-objective optimization problems. We consider thoroughly the feasible set of the fuzzy relation inequality systems in three possible cases, namely “≤”, “≥” and the combination of them. In general, their feasible sets are nonconvex which are completely determined by one vector as their maximum solution and a finite number of minimal solutions. The maximum and minimal solutions are formulated by using the additive generator. Additionally, we present some conditions for each case under which the problem can be reduced. Finally, each reduced problem is solved by the genetic algorithm and the efficiency of the proposed method is shown by some numerical examples.     

基于k阶二元关系的直觉模糊粗糙集

利用k阶二元关系定义直觉模糊粗糙集,讨论了分别为串行、自反、对称、传递关系时所对应的上、下近似算子的性质。在有限论域U中,研究了任一自反二元关系所诱导的直觉模糊拓扑空间中直觉模糊闭包、内部算子与相对应的上、下近似算子的关系。     

Eigenvector method,consistency test and inconsistency repairing for an incomplete fuzzy preference relation

In this paper, we extend the eigenvector method (EM) to priority for an incomplete fuzzy preference relation. We give a reasonable definition of multiplicative consistency for an incomplete fuzzy preference relation. We also give an approach to judge whether an incomplete fuzzy relation is acceptable or not. We develop the acceptable consistency ratio for an incomplete multiplicative fuzzy preference relation, which is simple and similar to Saaty’s consistency ratio (CR) for the multiplicative preference relation. If the incomplete fuzzy preference relation is not of acceptable consistency, we define a criterion to find the unusual and false element (UFE) in the preference relation, and present an algorithm to repair an inconsistent fuzzy preference relation until its consistency is satisfied with the consistency ratio. As a result, our improvement method cannot only satisfy the consistency requirement, but also preserve the initial preference information as much as possible. Finally, an example is illustrated to show that our method is simple, efficiency, and can be performed on computer easily.     

基于小波系数相对模糊关系的水印方法

提出一种基于带参数整数小波变换和相对小波系数模糊关系的数字水印算法.应用基于视觉系统小波域量化噪声的视觉权重分析方法,自适应的构造模糊关系矩阵,在水印的提取过程中实现了盲检测.本方法与经典的密码理论以及高级加密算法相结合.应用Rabin方法生成单向Hash函数,信息隐藏算法可以完全公开.水印算法不可感知性好,鲁棒性强,是一种有效的版权保护方法.     

Reconstruction families of possibilistic structure systems

A method is presented for characterizing the family of overall systems reconstructable from a given possibilistic structure system. The technique is elaborated in terms of fuzzy relation equations.It is demonstrated that the reconstruction family of a given structure system is equivalent to the set of solutions of a special type of fuzzy relation equation. The solution set is partially ordered, and contains both minimal solutions and a unique maximum solution. When these elements are identified, all members of the reconstruction family are determined.Another characteristic of the reconstruction family is its reconstruction uncertainty, a measure of which is also developed in this paper. This measure is used to define an identifiability quotient that expresses the degree of confidence with which we may identify a single overall system given a particular structure system.