On similarity measures of interval-valued intuitionistic fuzzy sets and their application to pattern recognitions

The concept of the degree of similarity between interval-valued intuitionistic fuzzy sets （IVIFSs） is introduced, and some distance measures between IVIFSs are defined based on the Hamming distance, the normalized Hamming distance, the weighted Hamming distance, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance, etc. Then, by combining the Hausdorff metric with the Hamming distance, the Euclidean distance and their weighted versions, two other similarity measures between IVIFSs, i. e., the weighted Hamming distance based on the Hausdorff metric and the weighted Euclidean distance based on the Hausdorff metric, are defined, and then some of their properties are studied. Finally, based on these distance measures, some similarity measures between IVIFSs are defined, and the similarity measures are applied to pattern recognitions with interval-valued intuitionistic fuzzy information.

Interval-Valued Intuitionistic Fuzzy-Rough Sets

This paper combines interval-valued intuitionistic fuzzy sets and rough sets.It studies rougheness in interval-valued intuitionistic fuzzy sets and proposes one kind of interval-valued intuitionistic fuzzy-rough sets models under the equivalence relation in crisp sets.That extends the classical rough set defined by Pawlak.

Interval-valued intuitionistic fuzzy aggregation operators

The notion of the interval-valued intuitionistic fuzzy set （IVIFS） is a generalization of that of the Atanassov＇s intuitionistic fuzzy set. The fundamental characteristic of IVIFS is that the values of its membership function and non-membership function are intervals rather than exact numbers. There are various averaging operators defined for IVlFSs. These operators are not monotone with respect to the total order of IVIFS, which is undesirable. This paper shows how such averaging operators can be represented by using additive generators of the product triangular norm, which simplifies and extends the existing constructions. Moreover, two new aggregation operators based on the t.ukasiewicz triangular norm are proposed, which are monotone with respect to the total order of IVIFS. Finally, an application of the interval-valued intuitionistic fuzzy weighted averaging operator is given to multiple criteria decision making.

Intuitionistic fuzzy C-means clustering algorithms

Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-means method the seeds are modified,and for each IFS a membership degree to each of the clusters is estimated.In the end of the algorithm,all the given IFSs are clustered according to the estimated membership degrees.Furthermore,the algorithm is extended for clustering interval-valued intuitionistic fuzzy sets(IVIFSs).Finally,the developed algorithms are illustrated through conducting experiments on both the real-world and simulated data sets.

Intuitionistic fuzzy hierarchical clustering algorithms

Intuitionistic fuzzy set （IFS） is a set of 2-tuple arguments, each of which is characterized by a membership degree and a nonmembership degree. The generalized form of IFS is interval-valued intuitionistic fuzzy set （IVIFS）, whose components are intervals rather than exact numbers. IFSs and IVIFSs have been found to be very useful to describe vagueness and uncertainty. However, it seems that little attention has been focused on the clustering analysis of IFSs and IVIFSs. An intuitionistic fuzzy hierarchical algorithm is introduced for clustering IFSs, which is based on the traditional hierarchical clustering procedure, the intuitionistic fuzzy aggregation operator, and the basic distance measures between IFSs： the Hamming distance, normalized Hamming, weighted Hamming, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance. Subsequently, the algorithm is extended for clustering IVIFSs. Finally the algorithm and its extended form are applied to the classifications of building materials and enterprises respectively.

Target Priority Determination Methods by Interval-Valued Intuitionistic Fuzzy Sets with Unknown Attribute Weights

The paper aims at the problem of multi-targets threat degree being hard to be evaluated accurately in complex air defense battlefield environments. Combined with multi-sensors information fusion and interval-valued intuitionistic fuzzy sets(IVIFS) theories, the target priority determination is studied. The score and accuracy functions of IVIFS are improved with thinking about the hesitating information in order to increase the rationality.Then, the influence factors of target priority and the nonlinear relationship between the influence factors and target priority are analyzed. Next, the algorithms for calculating the factor weights and sensor weights are given. Based on the theory of IVIFS and technique for order preference by similarity to an ideal solution(TOPSIS), two methods of target priority determination based on the IVIFS and TOPSIS are proposed. At last, an application example verifies the effectiveness and flexibility of the proposed algorithms.

NOTES ON “MULTICRITERIA FUZZY DECISION-MAKING METHOD BASED ON A NOVEL ACCURACY FUNCTION UNDER INTERVAL-VALUED INTUITIONISTIC FUZZY ENVIRONMENT”

This note points outs the inappropriateness of an accuracy function introduced by Ye [Ye, J. （2009）. Multicriteria fuzzy decision-making method based on a novel accuracy function under interval-valued intuitionistic fuzzy environment. Expert Systems with Applications, 36 （3）： 6899-6902] and its misleading use for comparing two interval-valued intuitionistic fuzzy numbers.

Multi-attribute decision making with Pythagorean fuzzy sets via conversions to intuitionistic fuzzy sets and ORESTE method

The aim of this paper is to study the conversions between Pythagorean fuzzy sets and Atanassov’s intuitionistic fuzzy sets.Besides,an ORESTE method based on multi-attribute decision making with Pythagorean fuzzy sets is developed by utilising the developed conversions.In this paper,according to the geometric representations of Pythagorean fuzzy sets and Atanassov’s intuitionistic fuzzy sets,two types of conversions between the two fuzzy sets are constructed,which are further used to derive information measures include entropy and cross-entropy measures of Pythagorean fuzzy sets.Then,by combining with the ORESTE method,a direct decision procedure for multi-attribute decision making with Pythagorean fuzzy information is developed.Finally,a numerical example of the evaluation of regional energy efficiency is shown to illustrate the feasibility and validity of the developed decision procedure.

T-S模的直觉模糊群及其运算

在直觉模糊群定义的基础上,将算子"∧"与"∨"分别推广到T模和S模上,从而定义了关于T-S模的直觉模糊群,给出了其在对偶模意义下的等价定义,并且证明了一个直觉模糊集构成关于T-S模的直觉模糊群的几个等价命题.最后,研究了这种直觉模糊群的一些基本运算问题.

直觉模糊S-粗决策模型及应用

基于直觉模糊S-粗集理论,提出双向直觉模糊S-粗决策模型。首先给出指标值的量化方法,将目标矩阵进行标准化处理。其次,建立直觉模糊S-粗决策的上-决策和下-决策优化模型,并给出直觉模糊S-粗决策算法及详细步骤。最后,以空袭目标为例,详细研究了目标威胁程度评估过程。结果表明,直觉模糊S-粗决策模型能够综合处理决策因素的定性与定量因素,得到的决策结果综合性能最优。所得的排序结果真实、准确地反映了实际情况。

基于GM(1,1)和D-S证据理论的直觉模糊应急决策方法

针对应急决策问题,提出1种基于GM(1,1)与D-S证据理论的直觉模糊决策方法。借助记分函数和犹豫度构建以直觉模糊数为建模对象的GM(1,1)模型,并基于该模型预测下一时间段的决策信息;根据直觉模糊熵权法确定指标权重,通过指标权重与直觉模糊集本身特点,构建D-S证据理论的基本概率分配函数,并根据D-S证据理论的信息融合规则进行方案的信息融合,进而对方案进行排序;通过某特大煤矿坍塌事件进行实证研究,验证了本文提出方法的合理性与可行性。

环上的(T,S)-直觉模糊理想

应用直觉模糊集到环理论,给出了环上(T,S)-模糊理想和(T,S)-直觉模糊理想的定义,研究了(T,S)-直觉模糊理想与普通理想的关系并讨论了其性质.

单向S-粗直觉模糊集

针对系统具有的模糊、不确定及动态特性,基于直觉模糊集和单向S-粗集理论,提出单向S-粗直觉模糊集,给出它的结构、性质、存在背景和意义解释。分析了单向S-粗直觉模糊集与Pawlak粗集,单向S-粗模糊集以及单向S-粗集等之间的关系。

基于直觉模糊S-粗集副集的目标合群算法

阐述直觉模糊S-粗集和直觉模糊S-粗集副集的数学结构与特性。针对空间群的动态性,用一个双向直觉模糊S-集合表示一个空间群,群中的动态目标构成直觉模糊S-粗集的副集。给出基于直觉模糊S-粗集副集的目标合群算法,对空间群中发现新目标、目标合群等动态操作过程进行分析。通过实例证明该方法的正确性和有效性,计算结果表明该方法能较灵活地处理目标编群中的动态问题。

直觉模糊S-粗集的一般结构及性质

在一般等价关系下,给出单向直觉模糊S-集合、双向直觉模糊S-集合的概念,利用这些概念,提出直觉模糊S-粗集,给出直觉模糊S-粗集的数学结构和有关结论,对有关性质进行了讨论。直觉模糊S-粗集为研究动态信息系统中大量的不确定性问题提供了新的途径和方法。

(T,S)-凸直觉模糊集

在K.Atanassov提出的直觉模糊集的概念基础上,利用T-范数和S-范数,定义了(T,S)-凸直觉模糊集,(T,S)-直觉模糊子空间和(T,S)-直觉模糊仿射集,并讨论了(T,S)-凸直觉模糊集的一些性质,(T,S)-直觉模糊子空间和(T,S)-直觉模糊仿射集之间的关系。

一种新的直觉模糊伪S-蕴涵

直觉模糊集在模糊推理、模糊评价、模糊控制和模糊决策中有很重要的应用,直觉模糊集的蕴涵构造研究是一个重要研究方向.在直觉模糊集理论基础之上,构造了一新的直觉模糊伪S-蕴涵,证明了其单调性、边界条件等系列重要性质,还讨论该蕴涵的一些代数性质.

基于加权IFS证据合成的多源信息融合方法

为权衡信息融合系统对各条证据的可信程度,提出基于直觉模糊交叉熵的证据折扣方法,用于直觉模糊证据合成规则。并根据确定性理论,对直觉模糊决策中的记分函数进行改进,得到基于确定性因子的记分函数形式,用于直觉模糊证据合成结果判定。最后通过与其他算法的对比实验,验证算法在多传感器目标识别应用中是有效的,且具有较高的计算效率。

直观模糊性特别拓扑空间中的S-可数性及S-Lindelof空间

在直观模糊特别拓扑空间中引入S-可数性及S -Lindelof空间的概念 ,一个直观模糊特别半开集族 {Aa｜a∈I}是 (x ,τ)的S-基 ,当且仅当每一个直观模糊特别集均为集族 {Aa｜a∈I}的元的并 ;每一满足S-第二可数性的直观模型特别拓扑空间也满足S-第一可数性公理 ;满足S -第二可数性公理的直观模糊特别拓扑空间是S-Lindelof空间 ;

考虑决策者风险偏好的区间直觉模糊多属性群决策方法

提出了一种考虑决策者风险偏好且属性权重信息不完全的区间直觉模糊数多属性群决策方法。同时考虑相似度和接近度,确定每一属性的决策者权重。为了考虑决策者风险偏好对决策结果的影响和避免区间直觉模糊矩阵的渐进性,引入了决策者风险偏好系数,将集结后的综合决策矩阵转换成区间数矩阵。然后,为了客观地求出属性权重信息不完全环境下属性的权重,构建了基于区间直觉模糊交叉熵的属性权重目标规划模型,该模型不仅考虑了评价值的偏差,也强调了评价值自身的可信度。最后,通过研发项目选择问题的实例分析说明了所提方法的合理性和优越性。