模糊互补判断矩阵排序的一种新方法

提出了模糊互补判断矩际排序的一种新方法，并将它同献[10]中给出的模糊互补判断矩阵的排序方法进行对比分析，找出了它们之间的关系，并指出了用本方法计算排序向量较合理。     

基于模糊结构元表述的模糊数排序

讨论了如何利用结构元理论来解决模糊数的排序问题.首先,给出了四种经典的模糊数排序方法,并证明了这四种方法都可以利用结构元理记来表述;进而,提出了一种基于结构元理论的排序方法,给出了该方法的性质,并同传统方法进行了比较.     

模糊判断矩阵排序向量的确定方法研究

首先给出模糊判断矩阵的两种一致性的定义。然后分析现有确定模糊判断矩阵排序向量的方法的特点及存在的问题，在此基础上，系统研究确定模糊判断矩阵排序向量的两类方法，第一类方法是先将模糊判断矩阵转化为AHP判断矩阵，然后将后者的排序向量作为前者的排序向量；另一类方法是直接由一致性或具有满意一致性的模糊判断矩阵计算排序向量。最后用算例说明所提出方法的应用。     

模糊一致矩阵排序方法的集成

基于文献[3]和文献[6]的模糊一致矩阵的排序方法,研究了这两种排序方法及排序向量的共性问题,给出了一种新的模糊一致矩阵排序方法的集成模型,得到了模糊一致矩阵排序方法及排序向量的相关结论和性质,丰富了模糊一致矩阵排序方法的理论和应用。     

三角模糊数互补判断矩阵的一种排序方法及其在项目投资决策中的应用

引入Y ager第三指标将模糊数非模糊化,将专家判断矩阵中的三角模糊数转化成精确数,再利用精确数互补判断矩阵的排序方法进行排序.并通过实例说明了方法的可行性和有效性.     

模糊权数与方案排序

讨论了由Fuzzy权数导出方案排序中的问题，提出了评价Fuzzy权数取值大小及取值集中程度的两个指标，并据此给出了由Fuzzy权数导出方案排序的方法。     

基于结构元最小二乘序的模糊线性回归

在具有模糊观测数据的线性回归问题中,通过定义模糊序指标实现模糊数的排序,借助经典最小二乘法原理,给出了使平方误差和在此排序方法下达到最小的模糊回归系数最小二乘序估计方法。三个例子的结果表明,文中的最小二乘方法能很好的对输入和输出为模糊数,回归系数为精确值的回归模型进行估计,更重要的是,此方法不仅对三角模糊数适用,对其他类型的模糊观测数据也适用。     

编序式问卷调查的模糊偏序排序法

讨论了现有的编序式问卷调查排序方法的不足之处;提出了基于模糊偏序关系的编序式问卷调查的排序方法,给出了模糊偏序排序法的数学模型,论证了用模糊偏序关系对编序式问卷调查进行排序是一种较好的方法;并用实例分析了模糊偏序排序法应用于编序式问卷调查的具体操作步骤.     

一种基于散度的模糊数排序方法

引用一种距离测度及模糊数的权重面积,建立了一种基于散度的模糊数排序指标.新的排序指标不仅引入了两个参考对象,即两个模糊数的极大和极小(M),(N),同时还考虑了模糊数本身的影响和决策者的决策态度.排序方法不仅计算简单、易于操作,而且还具有良好的性质.算例分析表明本文所提出的排序方法在一定程度上克服了现有方法的缺陷.     

基于概率的直觉模糊数排序方法及其应用

使用已知信息计算概率,充分利用原始信息来对犹豫部分做出判断,本文提出一种基于概率的直觉模糊数的排序方法,并将此方法与已有的三种不同类型的直觉模糊数排序方法作比较,结果表明基于概率的排序方法是有效的,并且更符合决策者的意愿,最后将基于概率的排序方法拓展到群决策应用中。     

Transitive fuzzy orderings of fuzzy numbers

In this paper we introduce a generalization of the Baas-Kwakernaak index by replacing the min operation in their definition by a t-norm. Some properties of the thus defined induced fuzzy ordering are established. In particular, it is shown that restrictions of the induced fuzzy ordering on some special classes of fuzzy numbers are reflexive fuzzy orders.     

Application of fuzzy decision making to the fuzzy finite element method

In this paper we present a new approach to handle uncertainty in the Finite Element Method. As this technique is widely used to tackle real-life design problems, it is also very prone to parameter-uncertainty. It is hard to make a good decision regarding design optimization if no claim can be made with respect to the outcome of the simulation. We propose an approach that combines several techniques in order to offer a total order on the possible design choices, taking the inherent fuzziness into account. Additionally we propose a more efficient ordering procedure to build a total order on fuzzy numbers.     

A new linear ordering of fuzzy numbers on subsets of {{\mathcal F}({\pmb{\mathbb{R}}}})

We introduce a novel linear order on every family of fuzzy numbers which satisfies the assumption that their modal values must be all different and must form a compact subset of . A distinct new feature is that our linear determined procedure employs the corresponding order of a class interval associated with a confidence measure which seems intuitively anticipated. It is worthy noting that although we start from an entirely different rationale, we introduce a fuzzy ordering which initially coincides with the one established earlier by Ramik and Rimanek. However, this fuzzy ordering does not apply when the supports of the fuzzy numbers overlap. In order to cover such cases we extent this initial fuzzy ordering to the “extended fuzzy order” (XFO). This new XFO method includes a possibility and a necessity measure which are compared with the widely accepted PD and NSD indices of D. Dubois and H. Prade. The comparison shows that our possibility and necessity measures comply better with our intuition.     

Ordering fuzzy sets over the real line: An approach based on decision making under uncertainty

The ordering of fuzzy sets over the real line is approached from the point of view of ordering intervals rather than ordering numbers. First, the maximax and maximin criteria which are commonly used for ordering intervals are expressed in terms of characteristic functions. These criteria and the Hurwicz criterion for decision making under complete ignorance are then reformulated in a manner that allows for their generalization to the fuzzy case. Transitivity is established for these ordering rules. A criterion based on the principle of diminishing marginal utility is also presented.     

Ranking fuzzy numbers with maximizing set and minimizing set

Up to now, these are five methods of ranking n fuzzy numbers in order, but these methods contain some confusions and occasionally conflict with intuition. This paper introduces the concept of maximizing set and minimizing set to decide the ordering value of each fuzzy number and uses these values to determine the order of the n fuzzy numbers. In addition, we give a method for calculating the ordering value of each fuzzy number with triangular, trapezoidal, and two-sided drum-like shaped membership functions.     

Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations

In [R.R. Yager, D.P. Filev, Operations for granular computing: Mixing words and numbers, in: Proceedings of the FUZZ-IEEE World Congress on Computational Intelligence, Anchorage, 1998, pp. 123–128] Yager and Filev introduced the Induced Ordered Weighted Averaging (IOWA) operator. In this paper, we provide some IOWA operators to aggregate fuzzy preference relations in group decision-making (GDM) problems. These IOWA operators when guided by fuzzy linguistic quantifiers allow the introduction of some semantics or meaning in the aggregation, and therefore allow for a better control over the aggregation stage developed in the resolution process of the GDM problems. In particular, we present the Importance IOWA (I-IOWA) operator, which applies the ordering of the argument values based upon the importance of the information sources; the Consistency IOWA (C-IOWA) operator, which applies the ordering of the argument values based upon the consistency of the information sources; and the Preference IOWA (P-IOWA) operator, which applies the ordering of the argument values based upon the relative preference values associated to each one of them. We provide a procedure to deal with ‘ties’ in respect to the ordering induced by the application of one of these IOWA operators; it consists of a sequential application of the above IOWA operators. We also present a selection process for GDM problems based on the concept of fuzzy majority and the above three IOWA operators. Finally, we analyse the reciprocity and consistency properties of the collective fuzzy preference relations obtained using IOWA operators.     

Mathematical developments arising from ‘semantical implication’ and the evaluation of membership characteristic functions

In this paper, the actuation of a semantical ordering in a property set is exposed. Such an ordering follows from the concept of semantical implication: the relation that takes place between two properties, when the values of the details forming one of these are the same as of the other excepting those which, in the implied property, are irrelevant. Mainly, the argumentation deals with the analysis of the operations which, due to the relations in subject, are allowed in the property set considered. This, with the direct purpose of attaining to the structure of the set itself.     

Fuzzy蕴涵代数的MP理想与正规MP理想

在Fuzzy蕴涵代数(简称FI代数)上引入了MP理想与正规MP理想的概念并给出了它们的等价刻画;探讨了FI代数的MP理想、偏序理想和正规MP理想间的多种关系, 证明了一个正则FI代数是可交换FI代数当且仅当它的每个MP理想都是正规理想,也当且仅当{0}是正规MP理想.     

Ranking fuzzy numbers by distance minimization

In this paper, we proposed a defuzzification using minimizer of the distance between the two fuzzy numbers. Then, we obtain the nearest point with respect to a fuzzy numbers and by considering the nearest point, we can present a ranking method for the fuzzy numbers. Also we give two new properties for ordering. Theorems and remarks are proposed for existence and uniqueness of the nearest point. The method is illustrated by numerical examples and compared with other methods.