p-平均对称度量关于代数运算的收敛问题

引入了广义 Fuzzy数的概念 ,讨论了 p-平均对称差度量 dΔp (参见 [3])在广义 Fuzzy数空间 E* 上关于代数运算的收敛问题。     

Fuzzy矩阵方程=■的解及性质

本文首先给出了矩阵方程Ax＝b的解的定义，然后对此解进行了深入的研究。给出了锥形Fuzzy集的概念，讨论了方程Ax－b与锥形Fuzzy集之间的关系。最后证明了一类锥形Fuzzy集全体构成完备的Fuzzy度量空间。     

Fuzzy多项式预测的数学模型的最小二乘估计

以LR型Fuzzy数空间的距离为基础,给出Fuzzy多项式预测的数学模型的最小二乘估计。     

度量模糊标志值离散程度的变异系数

继续[1],[2]的讨论，进一步探讨由Fuzzy数据资料计算变异系数的问题，提出了正有界闭Fuzzy数平均取值大小的评价指标，据此而给出了由表示为正有界闭Fuzzy数的数据资料计算变异系数的计算公式，还给出了数据资料的三角Fuzzy数和对称形Fuzzy数时变异系数的计算公式。     

Fuzzy值可测函数及其构造

本文的目的是引入Fuzzy值可测函数的一般概念并着重讨论它的构造。为此,首先给出Fuzzy数度量空间的一些主要性质;然后建立Fuzzy数可测空间并提出Fuzzy值可测函数的一般定义;最后讨论Fuzzy值可测函数的九种等价构造。     

Fuzzy代数直积的一些性质

首先给出了Fuzzy代数直积的一些性质,然后讨论了Fuzzy商代数的直积结构.     

复Fuzzy级数及其收敛性的进一步讨论

给出复 Fuzzy数项级数与复 Fuzzy函数级数的概念 ,讨论它们的收敛性问题 ,给出它们收敛性的相应判别法则。     

Fuzzy度量空间上Lipschitz型自映射的公共不动点

在Fuzzy度量空间上建立了一个Lipschitz型自映射的公共不动点定理。作为应用,得到Fuzzy度量空间上Bose型和Kannan-Reich型自映射的公共不动点定理,从而统一并推广了Bose与KannanReich在度量空间上的有关结论。     

线性Fuzzy方程组=的解

重点研究线性Fuzzy方程组=的解,其中矩阵和向量均以有限Fuzzy数为其元素。文中首先指出,使用扩展原理和Fuzzy数运算规则有时会导致=没有解。本文以实广义逆矩阵为工具,给出了=的六个解,证明了它们都是Rn上的Fuzzy向量。     

线性Fuzzy方程组(A~)(x~)=(b~)的解

重点研究线性Fuzzy方程组Ax=b的解,其中矩阵A和向量5均以有限Fuzzy数为其元素。文中首先指出,使用扩展原理和Fuzzy数运算规则有时会导致Ax=b没有解。本文以实广义逆矩阵为工具,给出了Ax=b的六个解,证明了它们都是R^n上的Fuzzy向量。     

工作分派问题的一种模糊决策模型

针对一类流水线式的工作分派问题,建立了在赋模糊权的二部图中求解模糊最大最小匹配的数学模型,给出了该模型的一个有效算法,并利用模糊决策思想得到了优化此类工作分派问题的一种决策方法     

Fuzzy数理论的几个重要定理

给出Fuzzy 数集上(下)确界的新定义,证明了确界定理、单调收敛定理、闭区间套定理及柯西收敛原理,从而得出了任何有界闭区间[a,b]都是完备的结果     

图的最大亏格、支配数和围长

一个连图G的最大亏格γM（G）=（β（G）-ξ（G）/2,其中β（G）=E（G）-V（G 1是G的圈秩,ξ（G）是G的Betti亏数,本文利用G的支配数和围长给出了G的Betti亏数ξ（G）的一个上界,从而也给出了最大亏格γ（M（G）的一个下界,而且它是可达的,对于某些图类,该下界比黄元秋（2000）所给下界更好。     

两类四角系统的匹配数与点独立集数

给出了两类四角系统的完美匹配数、匹配数和点独立集数的计算公式     

有理数乘群的两类子群及其相互关系

本文对任意的素数p 及自然数n,构造出有理数乘群G= (Q,·)的两类子群Gp,n和Gp,0,G关于它们的商群分别为Zn 和Z,它们之间有许多很好的关系,特别是其中的同构关系.这些对我们进一步认识有理数域及近世代数的入门教学有一定的参考价值.     

Fuzzy addition in the L-fuzzy real line

Under the hypothesis L is a chain, we construct a binary operation ⊕ on the L-fuzzy real line $\text{R}$(L) which reduces to the usual addition on $\text{R}$ if ⊕ is restricted to the embedded image of $\text{R}$ in $\text{R}$(L), which yields a partially ordered, abelian cancellation semigroup with identity, and which is jointly fuzzy continuous on $\text{R}$(L). We show ⊕ is unique, i.e. it is the only extension of addition to $\text{R}$(L) which is consistent. We study the relationship between ⊕ and other fuzzy continuous extensions of the usual addition. We also show that fuzzy translation is a weak fuzzy homeomorphism and, under certain conditions, a fuzzy homeomorphism.     

A type of fuzzy ring

In this study, by the use of Yuan and Lee’s definition of the fuzzy group based on fuzzy binary operation we give a new kind of fuzzy ring. The concept of fuzzy subring, fuzzy ideal and fuzzy ring homomorphism are introduced, and we make a theoretical study their basic properties analogous to those of ordinary rings.      

Editorial

Linear programming problems with fuzzy parameters are formulated by fuzzy functions. The ambiguity considered here is not randomness, but fuzziness which is associated with the lack of a sharp transition from membership to nonmembership. Parameters on constraint and objective functions are given by fuzzy numbers. In this paper, our object is the formulation of a fuzzy linear programming problem to obtain a reasonable solution under consideration of the ambiguity of parameters. This fuzzy linear programming problem with fuzzy numbers can be regarded as a model of decision problems where human estimation is influential.     

群,环上的Fuzzy关系

一些学者已对群和环上的Ｆｕｚｚｙ关系进行了研究，本文进一步研究了群、环上的Ｆｕｚｚｙ关系，得出了若干重要的结论。     

Toward extended fuzzy logic—A first step

Fuzzy logic adds to bivalent logic an important capability—a capability to reason precisely with imperfect information. Imperfect information is information which in one or more respects is imprecise, uncertain, incomplete, unreliable, vague or partially true. In fuzzy logic, results of reasoning are expected to be provably valid, or p-valid for short. Extended fuzzy logic adds an equally important capability—a capability to reason imprecisely with imperfect information. This capability comes into play when precise reasoning is infeasible, excessively costly or unneeded. In extended fuzzy logic, p-validity of results is desirable but not required. What is admissible is a mode of reasoning which is fuzzily valid, or f-valid for short. Actually, much of everyday human reasoning is f-valid reasoning.f-Valid reasoning falls within the province of what may be called unprecisiated fuzzy logic, FLu. FLu is the logic which underlies what is referred to as f-geometry. In f-geometry, geometric figures are drawn by hand with a spray pen—a miniaturized spray can. In Euclidean geometry, a crisp concept, C, corresponds to a fuzzy concept, f-C, in f-geometry. f-C is referred to as an f-transform of C, with C serving as the prototype of f-C. f-C may be interpreted as the result of execution of the instructions: Draw C by hand with a spray pen. Thus, in f-geometry we have f-points, f-lines, f-triangles, f-circles, etc. In addition, we have f-transforms of higher-level concepts: f-parallel, f-similar, f-axiom, f-definition, f-theorem, etc. In f-geometry, p-valid reasoning does not apply. Basically, f-geometry may be viewed as an f-transform of Euclidean geometry.What is important to note is that f-valid reasoning based on a realistic model may be more useful than p-valid reasoning based on an unrealistic model.