直觉模糊集的聚类方法研究

对基于直觉模糊集的聚类问题进行了研究．首先给出直觉模糊相似度的概念,并构建了直觉模糊相似矩阵和直觉模糊等价矩阵;然后定义了直觉模糊相似矩阵的合成运算法则．给出直觉模糊相似矩阵转化为直觉模糊等价矩阵的途径;此外．还分别定义了直觉模糊相似矩阵和直觉模糊等价矩阵的λ-截矩阵,进而给出了直觉模糊集的一种聚类方法;最后通过算例对该方法进行了说明和分析．     

Image fusion using intuitionistic fuzzy sets

Image fusion is the process of combining one or more images which are obtained from different environment into a single image which is more useful for further image processing tasks. Image registration and image fusion are of great importance in defence and civilian sectors, particularly for recognizing a ground/air force vehicle and medical imaging. In this paper a new way is drawn to fuse two or more images by using maximum, minimum operations in intuitionistic fuzzy sets (IFSs). IFSs are more suitable for image processing since every digital image have lot of uncertainties. In processing phase, images are reformed into intuitionistic fuzzy images (IFIs). Entropy is employed to obtain the optimum value of the parameter in membership and non-membership function. Then the resulting IFIs are disintegrated into image blocks and the corresponding blocks of the images are reunioned by finding the count of blackness and whiteness of the blocks. This paper evaluates the performance of simple averaging (AVG), principal component analysis (PCA), discrete wavelet transform (DWT), stationary wavelet transform (SWT), dual tree complex wavelet transform (DTCWT), multi-resolution singular value decomposition (MSVD), nonsubsampled contourlet transform (NSCT) and IFS (proposed method) in terms of various performance measure. The experimental and comparison results show that luminance and contrast is of great importance for image processing and prove that the proposed method is better than all other methods.     

On the Dengfeng–Chuntian similarity measure and its application to pattern recognition

Recently Dengfeng and Chuntian defined the first operational definition of a similarity measure for intuitionistic fuzzy sets and showed how it may be used in pattern recognition problems. Unfortunately the Dengfeng and Chuntian operator may give counter-intuitive results. We show how a simple modification of the Dengfeng–Chuntian operator may correct this problem. We illustrate the application of the modified similarity measure on a simple pattern recognition problem.     

t-Norm based cuts of intuitionistic fuzzy sets

We consider the system of intuitionistic fuzzy sets (IF-sets) in a universe X and study the cuts of an IF-set. Suppose a left continuous triangular norm is given. The t-norm based cut (level set) of an IF-set is defined in a way that binds the membership and nonmembership functions via the triangular norm. This is an extension of usual cuts of IF-sets. We show that the system of these cuts fulfils analogical properties as usual systems of cuts. However, it is not possible to reconstruct an IF-set from the system of t-norm based cuts.     

On characterization of intuitionistic fuzzy rough sets based on intuitionistic fuzzy implicators

This paper presents a general framework for the study of relation-based (I,T)-intuitionistic fuzzy rough sets by using constructive and axiomatic approaches. In the constructive approach, by employing an intuitionistic fuzzy implicator I and an intuitionistic fuzzy triangle norm T, lower and upper approximations of intuitionistic fuzzy sets with respect to an intuitionistic fuzzy approximation space are first defined. Properties of (I,T)-intuitionistic fuzzy rough approximation operators are examined. The connections between special types of intuitionistic fuzzy relations and properties of intuitionistic fuzzy approximation operators are established. In the axiomatic approach, an operator-oriented characterization of (I,T)-intuitionistic fuzzy rough sets is proposed. Different axiom sets characterizing the essential properties of intuitionistic fuzzy approximation operators associated with various intuitionistic fuzzy relations are explored.     

Some information measures for interval-valued intuitionistic fuzzy sets

A new information entropy measure of interval-valued intuitionistic fuzzy set (IvIFS) is proposed by using membership interval and non-membership interval of IvIFS, which complies with the extended form of Deluca-Termini axioms for fuzzy entropy. Then the cross-entropy of IvIFSs is presented and the relationship between the proposed entropy measures and the existing information measures of IvIFSs is discussed. Additionally, some numerical examples are given to illustrate the applications of the proposed entropy and cross-entropy of IvIFSs to pattern recognition and decision-making.     

Distance and similarity measures between intuitionistic fuzzy sets: A comparative analysis from a pattern recognition point of view

A detailed analysis of the distance and similarity measures for intuitionistic fuzzy sets proposed in the past is presented in this paper. This study aims to highlight the main theoretical and computational properties of the measures under study, while the relationships between them are also investigated. Along with the literature review, a comparison of the analyzed distance and similarity measures from a pattern recognition point of view in three different classification cases is also presented. Initially, some artificial counter-intuitive recognition cases are considered, while in a second phase real data from medical and well known pattern recognition benchmark problems are used to examine the discrimination abilities of the studied measures. Moreover, all the measures are applied in a face recognition problem for the first time and useful conclusions are drawn regarding the accuracy and confidence of the recognition results. Finally, the measures’ suitability and their drawbacks that make the development of more robust and efficient measures’ a still open issue are discussed.     

Clustering algorithm for intuitionistic fuzzy sets

The intuitionistic fuzzy set (IFS) theory, originated by Atanassov [K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems 20 (1986) 87-96], has been used in a wide range of applications, such as logic programming, medical diagnosis, pattern recognition, and decision making, etc. However, so far there has been little investigation of the clustering techniques of IFSs. In this paper, we define the concepts of association matrix and equivalent association matrix, and introduce some methods for calculating the association coefficients of IFSs. Then, we propose a clustering algorithm for IFSs. The algorithm uses the association coefficients of IFSs to construct an association matrix, and utilizes a procedure to transform it into an equivalent association matrix. The λ-cutting matrix of the equivalent association matrix is used to cluster the given IFSs. Moreover, we extend the algorithm to cluster interval-valued intuitionistic fuzzy sets (IVIFSs), and finally, demonstrate the effectiveness of our clustering algorithm by experimental results.     

Pattern recognition using type-II fuzzy sets

Type II fuzzy sets are a generalization of the ordinary fuzzy sets in which the membership value for each member of the set is itself a fuzzy set in [0,1]. We introduce a similarity measure for measuring the similarity, or compatibility, between two type-II fuzzy sets. With this new similarity measure we show that type-II fuzzy sets provide us with a natural language for formulating classification problems in pattern recognition.     

基于多参数的直觉模糊集相似度及其应用

有关直觉模糊集相似度的文献很多,但是现有的部分公式在处理实际问题时效果不理想。土耳其学者Boran和Akay提出了一种新的双参数相似度公式,改进了主流公式中存在的一些模式识别失灵问题。但是在他们引入隶属度、非隶属度、犹豫度取值对应等腰直角三角形的区域中,只考虑了斜边中线上的点。通过对这个公式改进,使情况一般化,并给出了一个更泛化的多参数公式,证明了其正确性,验证了其在模式识别中的有效性。     

基于覆盖的直觉模糊粗糙集

通过直觉模糊覆盖概念将覆盖粗糙集模型进行推广,提出一种基于直觉模糊覆盖的直觉模糊粗糙集模型.首先,介绍了直觉模糊集、直觉模糊覆盖和直觉模糊逻辑算子等概念;然后,利用直觉模糊三角模和直觉模糊蕴涵,构建两对基于直觉模糊覆盖的下直觉模糊粗糙近似算子和上直觉模糊粗糙近似算子;最后,给出了这些算子的基本性质并研究了它们之间的对偶性.     

Some geometric aggregation operators based on intuitionistic fuzzy sets

The weighted geometric (WG) operator and the ordered weighted geometric (OWG) operator are two common aggregation operators in the field of information fusion. But these two aggregation operators are usually used in situations where the given arguments are expressed as crisp numbers or linguistic values. In this paper, we develop some new geometric aggregation operators, such as the intuitionistic fuzzy weighted geometric (IFWG) operator, the intuitionistic fuzzy ordered weighted geometric (IFOWG) operator, and the intuitionistic fuzzy hybrid geometric (IFHG) operator, which extend the WG and OWG operators to accommodate the environment in which the given arguments are intuitionistic fuzzy sets which are characterized by a membership function and a non-membership function. Some numerical examples are given to illustrate the developed operators. Finally, we give an application of the IFHG operator to multiple attribute decision making based on intuitionistic fuzzy sets.     

变参数区间值直觉模糊集在模式识别中的应用

提出了带参数区间值直觉模糊集的概念,构造了一系列带参数的区间值直觉模糊集。从已知隶属度和非隶属度出发,重点分析了激励惩罚型变参数区间值直觉模糊集的构造。运用模式识别与医疗诊断案例进行对比。仿真实验结果表明,带参数区间值直觉模糊集方法比传统的直觉模糊集方法更具灵活性。     

On possible and necessary inclusion of intuitionistic fuzzy sets

The problem of measuring the degree of inclusion for two intuitionistic fuzzy sets (IF-sets) is considered. Since the nature of IF-sets includes uncertainty expressed both by the membership and nonmembership functions, this very feature should be also regarded in the construction of the subsethood measures. Therefore, we suggest not a single coefficient measuring the grade of subsethood but two indicators that leave some space to the mentioned uncertainty and correspond rather to necessary and possible inclusion.     

Personnel selection based on intuitionistic fuzzy sets

One of the most important activities carried out by human resource management is personnel selection, concerned with identifying an individual from a pool of candidates suitable for a vacant position. Traditionally, personnel selection is a group decision‐making problem under multiple criteria containing subjectivity, imprecision, and vagueness, which are best represented with fuzzy data. In this article, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method extended to intuitionistic fuzzy environments is proposed to select appropriate personnel among candidates. An intuitionistic fuzzy set (IFS), which is characterized by membership function, nonmembership function, and hesitation margin, is a more suitable way to deal with vagueness when compared to a fuzzy set. To demonstrate the applicability and effectiveness of the intuitionistic fuzzy TOPSIS method, a numerical example of personnel selection in a manufacturing company for a sales manager position is given. © 2011 Wiley Periodicals, Inc.     

一种新的直觉模糊集相似度测量

直觉模糊集IFS是模糊集的进一步推广,比传统的模糊集在处理模糊性和不确定性等方面更具灵活性和实用性。已有的部分相似度测量方法,不满足相似度的公理化或有些存在反直觉的情形。通过对Boran和AKay提出的直觉模糊集的相似性度量的研究,提出了一种新的改进的相似度测量公式,证明了该公式的合理性,最后通过实例说明了该方法的有效性。     

直觉模糊集新的熵公式及应用

针对直觉模糊集的熵的公理化定义中存在的问题,修正了直觉模糊熵的公理化定义,给出了一个新的直觉模糊集的熵的计算公式,证明了其正确性。给出了一个基于直觉模糊数的熵的多属性决策算法,应用实例表明其合理性和有效性。     

A patent infringement early-warning methodology based on intuitionistic fuzzy sets: A case study of Huawei

In the era of technology-driven economies, patent infringement has become one of the main risks faced by companies, which exists in all stages of technological innovation. However, the increasing size of patent information as well as the inherent fuzziness of patent infringement risk make the early warning of this risk a knowledge-intensive engineering activity. In this study, a novel patent infringement early warning methodology based on intuitionistic fuzzy sets (IFSs) is proposed to accurately evaluate and classify patent infringement risk for its management. First, a hierarchical indicator system of the methodology is established, including indicators of regional judicial and administrative protection. Then entropy weights for IFSs and intuitionistic fuzzy weighted geometric (IFWG) operators are utilized to objectively and automatically aggregate indicator data on early warning patents and their similar patents to evaluate IFS results, which is a multi-layer data processing structure. Finally, the normalized Euclidean distances are used to classify risk levels. In a case study, Huawei's historical patents are taken as the test data, and the methodology is verified by comparing the output results and classification with the actual litigation status. Managerial implications for design engineers and patent attorneys are discussed corresponding to various technological innovation stages.     

一种基于直觉模糊集相似度的多属性决策方法

直觉模糊集（ＩＦＳ）是对模糊集理论的一种扩充,能更好地处理模糊概念．首先给出一种新的直觉模糊集相似度;然后提出基于直觉模糊集相似度的多属性决策方法;最后通过线性目标规划模型和直觉模糊集相似度,得到属性的最优权重和相应的方案排序．数值实例表明,该方法是有效而可行的．     

Entropy, similarity measure of interval-valued intuitionistic fuzzy sets and their applications

In this paper we propose an entropy measure for interval-valued intuitionistic fuzzy sets, which generalizes three entropy measures defined independently by Szmidt, Wang and Huang, for intuitionistic fuzzy sets. We also give an approach to construct similarity measures using entropy measures for interval-valued intuitionistic fuzzy sets. In particular, the proposed entropy measure for interval-valued intuitionistic fuzzy sets can yield a similarity measure. Several illustrative examples are given to demonstrate the practicality and effectiveness of the proposed formulas. We apply the similarity measure to solve problems on pattern recognitions, multi-criteria fuzzy decision making and medical diagnosis.