基于IITFN输入的复杂系统关联MAGDM方法

区间直觉梯形模糊数（Interval-valued intuitionistic trapezoidal fuzzy number,IITFN）是刻画复杂系统不确定性的有效工具. 基于进一步完善的IITFN 运算规则,讨论其局部封闭性. 由此定义IITFN 几何Bonferroni 平均算子,并验证该算子的相关性质. 针对决策者及属性之间均存在关联作用且权重均未知的多属性群决策（Multi-attribute group decision making,MAGDM）问题,提出基于前景混合区间直觉梯形几何 Bonferroni （Prospect hybrid interval-valued intuitionistic trapezoidal fuzzy geometric Bonferroni,PHIITFGB）平均算子 的关联多属性群决策方法. 该方法首先通过依次定义IITFN 的前景效应、前景价值函数和前景价值,获取前景价值矩阵;其次,将前景价值矩阵转化为前景记分函数矩阵,并综合运用基于灰关联深度系数的客观属性权重极大 熵模型和基于2-可加模糊测度与Choquet 积分联合的决策者权重确定模型,获取决策者权重及属性权重;再次,利 用PHIITFGB 算子集结各决策者的方案评估信息,结合决策者权重即可获取相应于各方案的综合前景价值;最后,计算综合前景记分价值函数,基于IITFN 的序关系判别准则确定方案排序. 案例验证决策方法的有效性和可行性.     

Support-intuitionistic模糊集集成算子及其决策应用

介绍Support-intuitionistic模糊集(SIFS)的概念,定义SIFS运算法则和客观权重,在此基础上提出SIFS加权算术平均算子和加权几何平均算子并证明相关定理。建立基于SIFS模糊信息的决策方法及定义SIFS得分函数和精确函数,利用SIFS支持度确定目标属性的权重使得属性权重成为客观值,加强集成结果的客观性。通过实例验证了该决策方法适用于处理基于SIFS模糊信息环境的决策问题。     

A Probability-Based Approach to Comparison of Fuzzy Numbers and Applications to Target-Oriented Decision Making

In this paper, we introduce a new comparison relation on fuzzy numbers based on their alpha-cut representation and comparison probabilities of interval values. Basically, this comparison process combines a widely accepted interpretation of fuzzy sets together with the uncertain characteristics inherent in the representation of fuzzy numbers. The proposed comparison relation is then applied to the issue of ranking fuzzy numbers using fuzzy targets in terms of target-based evaluations. Some numerical examples are used to illuminate the proposed ranking technique as well as to compare with previous methods. More interestingly, according to the interpretation of the new comparison relation on fuzzy numbers, we provide a fuzzy target-based decision model as a solution to the problem of decision making under uncertainty, with which an interesting link between the decision maker's different attitudes about target and different risk attitudes in terms of utility functions can be established. Moreover, an application of the proposed comparison relation to the fuzzy target-based decision model for the problem of fuzzy decision making with uncertainty is provided. Numerical examples are also given for illustration.     

A Novel Improved Accuracy Function for Interval Valued Pythagorean Fuzzy Sets and Its Applications in the Decision‐Making Process

The objective of this work is to present an improved accuracy function for the ranking order of interval‐valued Pythagorean fuzzy sets (IVPFSs). Shortcomings of the existing score and accuracy functions in interval‐valued Pythagorean environment have been overcome by the proposed accuracy function. In the proposed function, degree of hesitation between the element of IVPFS has been taken into account during the analysis. Based on it, multicriteria decision‐making method has been proposed for finding the desirable alternative(s). Finally, an illustrative example for solving the decision‐making problem has been presented to demonstrate application of the proposed approach.     

Comparison of Arithmetic Operations of Generalized Fuzzy Numbers: Case Study in Risk Assessment

This study attempts to perform arithmetic operations of generalized triangular fuzzy numbers apart from the existing approaches. Also, average width of generalized fuzzy numbers is defined. Numerical examples are illustrated and uncertainty is measured by using defined average width, and finally, the results are compared with the existing approaches. A case study for health risk assessment is carried out in this comparison.     

Entropy and Cross‐entropy for Generalized Hesitant Fuzzy Information and Their Use in Multiple Attribute Decision Making

In this paper, we present the entropy, cross‐entropy, and similarity measure for generalized hesitant fuzzy information and discuss their desirable properties. Some measure formulas are developed, and the relationships among them are investigated. We show that the similarity measure and entropy for generalized hesitant fuzzy information can be transformed by each other based on their axiomatic definitions. Then we develop two approaches for solving multiple attribute decision making, in which the attribute values are given in the form of generalized hesitant fuzzy elements (GHFEs). In the first approach, the attribute weight vector is determined by the generalized hesitant fuzzy entropies, and the optimal alternative is obtained by comparing the generalized hesitant fuzzy cross‐entropies between alternatives and positive‐ideal or negative‐ideal solutions; in the second approach, the attribute weight vector is derived from the maximizing deviation method and optimal alternative is obtained by using the technique for order preference by similarly to ideal solution (TOPSIS) method. Finally, an example is provided to illustrate the practicality and effectiveness of the developed approaches.     

A Novel Approach for Linguistic Group Decision Making Based on Generalized Interval‐Valued Intuitionistic Fuzzy Linguistic Induced Hybrid Operator and TOPSIS

In the multiple attribute linguistic group decision making analysis with interval‐valued intuitionistic fuzzy linguistic information, seeking highly efficient aggregation method and order relation play a crucial role. In this paper, we redefine an interval‐valued intuitionistic fuzzy linguistic variable that considers principal component and propose generalized interval‐valued intuitionistic fuzzy linguistic induced hybrid aggregation (GIVIFLIHA) operator with entropic order‐inducing variable and interval‐valued intuitionistic fuzzy linguistic technique for order preference by similarity to an ideal solution (TOPSIS) order relation based on interval‐valued intuitionistic fuzzy linguistic distance measure. Then, some primary properties of the GIVIFLIHA operator are discussed, and a linguistic group decision‐making approach based on GIVIFLIHA operator and interval‐valued intuitionistic fuzzy linguistic TOPSIS order relation is proposed. Finally, a numerical example concerning the investment strategy is given to illustrate the validity and applicability of the proposed method, and then the method is compared with the existing method to further illustrate its flexibility.     

Fuzzy Linguistic Induced Generalized OWA Operator and Its Application in Fuzzy Linguistic Decision Making

With respect to multiple attribute group decision making problems, in which the attribute values take the form of fuzzy linguistic scale variables, some decision analysis approaches are proposed. In this paper, we introduce the fuzzy linguistic induce generalized ordered weighted averaging operator and study some of its main properties by utilizing some operational laws of fuzzy linguistic scale variables. We end the paper with a numerical example of the new approach in a fuzzy linguistic decision making.     

Monte-Carlo-Type Techniques for Processing Interval Uncertainty,and Their Potential Engineering Applications

In engineering applications, we need to make decisions under uncertainty. Traditionally, in engineering, statistical methods are used, methods assuming that we know the probability distribution of different uncertain parameters. Usually, we can safely linearize the dependence of the desired quantities y (e.g., stress at different structural points) on the uncertain parameters x _i–thus enabling sensitivity analysis. Often, the number n of uncertain parameters is huge, so sensitivity analysis leads to a lot of computation time. To speed up the processing, we propose to use special Monte-Carlo-type simulations.     

模糊知识的不同否定及其在投资决策中的应用(英文)

在模糊知识中存在三种不同的否定,即矛盾否定、对立否定和中介否定,基于中介谓词逻辑MF与其无穷值语义解释Φ,研究了模糊知识及其三种否定的表示与推理,并在一个金融投资决策实例中进行了应用。引入了一种新的与Φ中参数λ相关的模糊产生式规则,讨论了实例中的模糊知识及其三种不同否定的推理算法与实现。     

Generalized Bonferroni Mean Operator for Fuzzy Number Intuitionistic Fuzzy Sets and its Application to Multiattribute Decision Making

The Bonferroni mean (BM) was originally introduced by Bonferroni in 1950. A prominent characteristic of BM is its capability to capture the interrelationship between input arguments. This makes BM useful in various application fields, such as decision making, information retrieval, pattern recognition, and data mining. In this paper, we examine the issue of fuzzy number intuitionistic fuzzy information fusion. We first propose a new generalized Bonferroni mean operator called generalized fuzzy number intuitionistic fuzzy weighted Bonferroni mean (GFNIFWBM) operator for aggregating fuzzy number intuitionistic fuzzy information. The properties of the new aggregation operator are studied and their special cases are examined. Furthermore, based on the GFNIFWBM operator, an approach to deal with multiattribute decision‐making problems under fuzzy number intuitionistic fuzzy environment is developed. Finally, a practical example is provided to illustrate the multiattribute decision‐making process.     

Pythagorean模糊幂Bonferroni集成算子及其决策应用

结合幂平均与Bonferroni平均集成算子的优点,定义了毕达哥拉斯模糊幂Bonferroni平均和毕达哥拉斯模糊加权幂Bonferroni平均集成算子,其不仅考虑了数据信息之间的整体均衡性,还考虑了属性之间可能存在的相互关联关系。研究了这些集成算子的优良性质和特殊情形,并在此基础上提出了一种属性间存在相关性的毕达哥拉斯模糊多属性决策方法。将其应用于国内航空公司的服务质量评价中,并与现有方法进行分析比较,验证了所提方法的有效性和可行性。     

Intuitionistic Fuzzy Geometric Bonferroni Means and Their Application in Multicriteria Decision Making

As a useful aggregation technique, the Bonferroni mean (BM) can capture the interrelationship between input arguments and has been a hot research topic recently. Based on the classic BM, many BM operators have been proposed and developed, such as the weighted BM, the generalized BM, the intuitionistic fuzzy BM, and so on. However, these BM operators are all based on the averaging mean, which is one of the basic aggregation approaches and focuses on the group opinion and another basic one is the geometric mean, which gives more importance to the individual opinions. To combine with the geometric mean and the BM, in this paper, we propose the geometric BM, the weighted geometric BM, and the generalized weighted geometric BM. These new geometric BMs can reflect the geometric interrelationship between the individual criterion and other criteria and keep the main advantage of BM. Furthermore, we investigate the geometric BMs under the intuitionistic fuzzy environment, which is more common phenomenon in modern life and develop three intuitionistic fuzzy geometric Bonferroni mean operators, i.e., the intuitionistic fuzzy geometric Bonferroni mean (IFGBM), the intuitionistic fuzzy weighted geometric Bonferroni mean (IFWGBM), and the intuitionistic fuzzy generalized weighted geometric Bonferroni mean (IFGWGBM) and study their desirable properties, such as idempotency, commutativity, monotonicity, and boundedness. Finally, on the basis of the IFWGBM and IFGWGBM operators, we propose an approach to multicriteria decision making under the intuitionistic fuzzy environment, and a practical example is provided to illustrate our results.     

MQDM: An Iterative Fuzzy Method for Group Decision Making in Structured Social Networks

Modern human societies have evolved into an almost entirely connected world, giving place to a remarkable increase in social interactions. In this new context and because of the globalization of all human activities, the collective participation in decision‐making processes takes an increasingly prominent role. In this paper, a method for group decision making from a set of imprecise opinions called “moviQuest Decision Making” (MQDM), is presented. This method allows to integrate the opinions of heterogeneous groups of agents in a structured social network along a sequence of voting rounds for collective decision making.     

Recent Deep Learning Techniques,Challenges and Its Applications for Medical Healthcare System: A Review

Neural Processing Letters - The concept of deep learning originates from artificial neural networks which has become a very popular research area during the past few decades. There are two main...     

Correction to: Recent Deep Learning Techniques,Challenges and Its Applications for Medical Healthcare System: A Review

Neural Processing Letters - A correction to this paper has been published: https://doi.org/10.1007/s11063-021-10527-5     

Theoretical Drawbacks in Fuzzy Ranking Methods and Some Suggestions for a Meaningful Comparison: An Application to Fuzzy Risk Analysis

Comparing fuzzy numbers has become one of the key challenging issues in fuzzy sets. Many researchers have proposed different procedures to rank fuzzy numbers, but the results frequently contradict each other. This work addresses the key weakness of several fuzzy ranking methods which have been introduced in recent articles. In this paper, we reveal that in nearly all ranking methods that the ranking procedure depends on the height of fuzzy numbers the result is unreliable. However, the final aim of this study is to propose some suggestions for a meaningful comparison in new methods. We demonstrate our reasoning with several numerical cases taken from literature.     

Towards Combining Probabilistic and Interval Uncertainty in Engineering Calculations: Algorithms for Computing Statistics under Interval Uncertainty, and Their Computational Complexity

In many engineering applications, we have to combine probabilistic and interval uncertainty. For example, in environmental analysis, we observe a pollution level x(t) in a lake at different moments of time t, and we would like to estimate standard statistical characteristics such as mean, variance, autocorrelation, correlation with other measurements. In environmental measurements, we often only measure the values with interval uncertainty. We must therefore modify the existing statistical algorithms to process such interval data.In this paper, we provide a survey of algorithms for computing various statistics under interval uncertainty and their computational complexity. The survey includes both known and new algorithms.