基于直觉模糊集的全区间决策方法

针对属性值为直觉模糊集且属性权重已知的模糊多属性决策问题,本文基于直觉模糊算术加权平均算子,提出了一种基于直觉模糊集的全区间决策方法。全区间决策函数引入了态度指标k,从而可以反映决策者态度的变化,从0到1变化k值,可以在整个区间内挖掘决策信息的变化,与得分函数法和基于距离TOPSIS贴近度方法相比,将过去的点值判断延伸至全区间判断,避免了决策信息的丢失现象,决策更加准确合理。实例计算表明该方法的正确性、有效性和合理性,具有一定的推广借鉴价值。     

Operation properties and -equalities of complex fuzzy sets

A complex fuzzy set is a fuzzy set whose membership function takes values in the unit circle in the complex plane. This paper investigates various operation properties and proposes a distance measure for complex fuzzy sets. The distance of two complex fuzzy sets measures the difference between the grades of two complex fuzzy sets as well as that between the phases of the two complex fuzzy sets. This distance measure is then used to define δ-equalities of complex fuzzy sets which coincide with those of fuzzy sets already defined in the literature if complex fuzzy sets reduce to real-valued fuzzy sets. Two complex fuzzy sets are said to be δ-equal if the distance between them is less than 1-δ. This paper shows how various operations between complex fuzzy sets affect given δ-equalities of complex fuzzy sets. An example application of signal detection demonstrates the utility of the concept of δ-equalities of complex fuzzy sets in practice.     

Fuzzy sets and type 2 under algebraic product and algebraic sum

The concept of fuzzy sets of type 2 has been proposed by L.A. Zadeh as an extension of ordinary fuzzy sets. A fuzzy set of type 2 can be defined by a fuzzy membership function, the grade (or fuzzy grade) of which is taken to be a fuzzy set in the unit interval [0, 1] rather than a point in [0, 1].This paper investigates the algebraic properties of fuzzy grades (that is, fuzzy sets of type 2) under the operations of algebraic product and algebraic sum which can be defined by using the concept of the extension principle and shows that fuzzy grades under these operations do not form such algebraic structures as a lattice and a semiring. Moreover, the properties of fuzzy grades are also discussed in the case where algebraic product and algebraic sum are combined with the well-known operations of join and meet for fuzzy grades and it is shown that normal convex fuzzy grades form a lattice ordered semigroup under join, meet and algebraic product.     

Algebras of fuzzy sets

In this paper we investigate two kinds of algebras of fuzzy sets, which are obtained by using Zadeh's extension principle. We give conditions under which a homomorphism between two algebras induces a homomorphism between corresponding algebras of fuzzy sets. We prove that if the structure of truth values is a complete residuated lattice, the induced algebra of a subalgebra of an algebra can be embedded into the induced algebra of fuzzy sets of . For direct products we give conditions under which the direct product of algebras of fuzzy sets could be embedded into the algebra of fuzzy sets of the direct product. In the case of homomorphisms and direct products, the two kinds of algebras of fuzzy sets behave in different ways.     

基于直觉模糊集的电子商务第三方物流选择

论文分析了电子商务第三方物流供应商的特点,提出电子商务第三方物流选择评估指标系统。由于指标评估的不确定性、语言描述性,以及信息的不完全性,引入直觉模糊集恰当地刻画已有信息关于相应决策指标的支持、反对,以及不确定状态,提出直觉模糊集的均衡数量集中函数以及均衡数量离散函数,并在此基础上,定义直觉模糊集合成函数,建立基于直觉模糊集的综合评估方法。并通过实例说明该模型的有效性。     

R0-代数（NM-代数）的区间值模糊子代数

将区间值模糊集的概念应用于R0-代数,引入区间值模糊R0-子代数的概念并研究它的性质。给出了区间值模糊集成为区间值模糊R0-子代数的一个充要条件;讨论了区间值模糊R0-子代数和R0-子代数之间的关系;定义了区间值模糊集的象和原象,获得了区间值模糊R0-子代数的象和原象成为区间值模糊R0-子代数的条件。     

Two fuzzier implication operators in the theory of fuzzy power sets

The theory of fuzzy power sets requires the use of an implication operator acting within the set of values taken by the membership functions of the fuzzy sets. Two such operators and resulting relationships between fuzzy sets are studied here, and the results compared with previous ones obtained with other implication operators.     

An interactive fuzzy satisficing method for multiobjective 0–1 programming problems with fuzzy numbers through genetic algorithms with double strings

In this paper, by considering the experts' vague or fuzzy understanding of the nature of the parameters in the problem-formulation process, multiobjective 0–1 programming problems involving fuzzy numbers are formulated. Using the a-level sets of fuzzy numbers, the corresponding nonfuzzy α-programming problem is introduced. The fuzzy goals of the decision maker (DM) for the objective functions are quantified by eliciting the corresponding linear membership functions. Through the introduction of an extended Pareto optimality concept, if the DM specifies the degree α and the reference membership values, the corresponding extended Pareto optimal solution can be obtained by solving the augmented minimax problems through genetic algorithms with double strings. Then an interactive fuzzy satisficing method for deriving a satisficing solution for the DM efficiently from an extended Pareto optimal solution set is presented. An illustrative numerical example is provided to demonstrate the feasibility and efficiency of the proposed method.     

On many-valued logics,fuzzy sets,fuzzy logics and their applications

This paper gives a survey of some aspects of many-valued logics and the theory of fuzzy sets and fuzzy reasoning, as advocated in particular by Zadeh. It starts with a short discussion of the development of many-valued logics and its philosophical background. In particular, the systems of Lukasiewicz and their algebraic models are presented. In connection with the famous Arrow paradoxon, Boolean valued and fuzzy social orderings are discussed. After some remarks on inference, fuzzy sets are introduced and it is shown that their definition is sound if some acceptable rationality requirements are demanded. Deformable prototypes are suggested in order to obtain the numerical values of the membership function for some applications. Finally, a recent paper of Bellman and Zadeh on a fuzzy logic, where the truth values themselves are fuzzy, is reviewed.     

基于直觉犹豫模糊集的TOPSIS法

直觉犹豫模糊集集成了直觉模糊集和犹豫模糊集的优势,能更有效地刻画决策者偏好不一致的情况。距离测度一直是研究的热点问题,但尚没有文献研究直觉犹豫模糊集间的距离测度,因此本文定义了直觉犹豫模糊集间的Hamming距离、Euclidean距离和广义距离,同时考虑每个元素的权重,定义了加权距离。犹豫度是直觉犹豫模糊集的重要特性,因此在考虑犹豫度的基础上,又定义了一些距离测度。这些距离测度不仅考虑了直觉犹豫模糊数间的差异,同时考虑了犹豫度的影响,决策者可以根据对直觉犹豫模糊数和犹豫度之间偏好的不同,设置不同的偏好值得到距离测度。然后基于这些距离测度,又提出了直觉犹豫模糊环境下的TOPSIS法。最后通过实例说明了所提出的TOPSIS法的合理性与实用性。     

可列集合套

[1]中给出模糊集的分解定理与表现定理，指出可利用集合套H＝{H（λ）|λ∈[0,1]}刻化模糊集。本文定义可列集合套H＝{H（a）|a∈Q}，Q是（0，1）的可列稠密子集。相应地给出新的分解定理与表现定理。指出可利用可列集合套刻化的模糊集。     

Hierarchical hesitant fuzzy K-means clustering algorithm简

Due to the limitation and hesitation in one＇s knowledge, the membership degree of an element to a given set usually has a few different values, in which the conventional fuzzy sets are invalid. Hesitant fuzzy sets are a powerful tool to treat this case. The present paper focuses on investigating the clustering technique for hesitant fuzzy sets based on the K-means clustering algorithm which takes the results of hierarchical clustering as the initial clusters. Finally, two examples demonstrate the validity of our algorithm.     

Modelling Fuzzy Control Problems with Toll Sets

We shall introduce in this paper toll sets and apply it to dynamical and control systems. We shall reformulate viability theorems for 'fuzzy controlled problem". 'Fuzzification" of dynamical and control systems led us to use indicators instead of characteristic functions for imbedding subsets into functions. Therefore, instead of obtaining the genuine fuzzy sets by a convexification procedure, we obtain the 'toll sets", the membership function being a cost function taking values between 0 and . Hence we can define 'fuzzy" or 'toll" – differential inclusions and control systems and solve the viability problem or the toll viability problem by building fuzzy controllers which regulate viable solutions (satisfying at each instant viability and toll viability constraints).     

Fuzzy Topological Representation Theory For D_(01)

In this paper, we shall introduce the concepts of fuzzy prime ideal, fuzzy Stone space, fuzzy fundamental set, etc. And the representations of a distributive lattice with 0, 1 (D_(01) denotes the equational class of such lattices) by fuzzy sets will be investigated and many useful results are obtained.     

Similarities in Fuzzy Regression Models

The solutions of a fuzzy regression model are obtained by converting the problem into a linear programming problem. For each level h, h[0, 1), there exists a solution. In this paper, we study the set of all the solutions to the fuzzy regression model that comes from a set of data as a metric space with an appropriate metric on it. We define a similarity ratio that allows us to compare the spaces of solutions of a fuzzy regression model that come from different sets of data. We also give an application using data sets concerning the GNP–money relationship.     

Convergence of powers of a fuzzy matrix

A Boolean matrix is a matrix with elements having values of either 1 or 0; a fuzzy matrix is a matrix with elements having values in the closed interval [0, 1]. Fuzzy matrices occur in the modeling of various fuzzy systems, with products usually determined by the “max(min)” rule arising from fuzzy set theory. In this paper, some sufficient conditions for convergence under “max(min)” products of the powers of a square fuzzy matrix and of a fuzzy state process are established.     

(∈,∈∨q)-n维凸模糊集

利用n维模糊集截集理论和模糊点与n维模糊集的邻属关系,并利用n+1-值Lukasiewicz蕴涵,首先给出(α,β)-n维凸模糊集的定义,然后对(∈,∈)-n维凸模糊集和(∈,∈∨q)-n维凸模糊集这两种非常有意义的n维凸模糊集进行了讨论,最后得到了一些有意义的结果。这将为n维凸模糊分析理论研究打下基础。     

Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems

An approach to dealing with missing data, both during the design and normal operation of a neuro-fuzzy classifier is presented in this paper. Missing values are processed within a general fuzzy min–max neural network architecture utilising hyperbox fuzzy sets as input data cluster prototypes. An emphasis is put on ways of quantifying the uncertainty which missing data might have caused. This takes a form of classification procedure whose primary objective is the reduction of a number of viable alternatives rather than attempting to produce one winning class without supporting evidence. If required, the ways of selecting the most probable class among the viable alternatives found during the primary classification step, which are based on utilising the data frequency information, are also proposed. The reliability of the classification and the completeness of information is communicated by producing upper and lower classification membership values similar in essence to plausibility and belief measures to be found in the theory of evidence or possibility and necessity values to be found in the fuzzy sets theory. Similarities and differences between the proposed method and various fuzzy, neuro-fuzzy and probabilistic algorithms are also discussed. A number of simulation results for well-known data sets are provided in order to illustrate the properties and performance of the proposed approach.