A New Ordered Weighted Averaging Operator to Obtain the Associated Weights Based on the Principle of Least Mean Square Errors

Determining OWA (ordered weighted averaging) weights has received more and more attention since the appearance of the OWA operator. Based on the principle of least mean squared errors, a new parametric OWA operator is proposed to obtain its associated weights. In coordination with fuzzy inference and a few of judgments on weights provided by decision makers (DMs), the new operator is carefully designed to avoid some problems of the existing ones, such as uncertainty in determining an objective function and the measure of orness, etc. Some properties of the problem are discussed to guarantee reliability in theory. A real‐life problem and two simulation experiments are performed to investigate its efficiency. All results show that the proposed operator can be a useful tool to express DMs’ preference information flexibly and objectively.     

On Solving Some Paradoxes Using the Ordered Weighted Averaging Operator Based Decision Model

In real life, people often need to make decisions in face of uncertainty. The traditional approach to rational decision making under uncertainty is the theory of expected utility. However, the Ellsberg paradox shows that the preference pattern exhibited by ordinary people often violates the expected utility theory. Similar to the Ellsberg paradox, Machina proposes two additional paradoxes, which challenge various important nonexpected utility models developed in the literature. This paper attempts to provide a unified treatment of all these paradoxes by extending the ordered weighted averaging operator based decision model to allow the degree of optimism to take multiple values instead of a single value.     

Exploring the Ordered Weighted Averaging Operator Knowledge Domain: A Bibliometric Analysis

Ordered weighted averaging (OWA) operator has been received increasingly widespread interest since its appearance in 1988. Recently, a topic search with the keywords “ordered weighted averaging operator” or “OWA operator” on Web of Science (WOS) found 1231 documents. As the publications about OWA operator increase rapidly, thus a scientometric analysis of this research field and discovery of its knowledge domain becomes very important and necessary. This paper studies the publications about OWA operator between 1988 and 2015, and it is based on 1213 bibliographic records obtained by using topic search from WOS. The disciplinary distribution, most cited papers, influential journals, as well as influential authors are analyzed through citation and cocitation analysis. The emerging trends in OWA operator research are explored by keywords and references burst detection analysis. The research methods and results in this paper are meaningful for researchers associated with OWA operator field to understand the knowledge domain and establish their own future research direction.     

基于有序加权平均算子的概率模糊认知图

模糊认知图(FCM)与概率模糊认知图(PFCM)使用简单的加权和集结因果推理结果,忽略了原因节点间关联关系的不确定性,阈值函数导致推理结果进一步失真.在继承FCM与PFCM优点的基础上,引入有序加权平均(OWA)算子模拟各种确定的或模糊的与或组合关系,提出了基于有序加权平均算子的概率模糊认知图(OWA-PFCM).通过构建一个动态的攻击效能评估模型,阐述了OWA-PFCM在工程建模中的应用.OWA-PFCM能同时表示因果节点状态的不确定性、因果联系强度的不确定性、与或组合关系的不确定性,具有更强的模拟能力.     

Fuzzy Entropic Ordered Weighted Averaging Operator and Its Application in Group Decision Making

In the paper, we develop a new method for multiple attribute group decision making) for fuzzy numbers. The fuzzy entropic weighted averaging (FEOWA) operator is an extension of the entropic ordered weighted averaging operator, which unifies the fuzzy entropy and the ordered weighted averaging operator in the same formulation. Then, some of its main properties by utilizing some operational laws of fuzzy numbers are studied. We also present the generalized entropic ordered weighted averaging operator and the fuzzy generalized entropic ordered weighted averaging operator. Moreover, a method based on the FEOWA operator for decision making is presented. Finally, a numerical example illustrates the applicability and effectiveness of the proposed method.     

Ordered Weighted Averaging Operators 1988–2014: A Citation‐Based Literature Survey

This study surveys the ordered weighted averaging (OWA) operator literature using a citation network analysis. The main goals are the historical reconstruction of scientific development of the OWA field, the identification of the dominant direction of knowledge accumulation that emerged since the publication of the first OWA paper, and to discover the most active lines of research. The results suggest, as expected, that Yager's paper 1 (IEEE Trans. Systems Man Cybernet, 18(1), 183–190, 1988) is the most influential paper and the starting point of all other research using OWA. Starting from his contribution, other lines of research developed and we describe them.     

基于IOWC-GOWA算子的区间组合预测模型

在连续区间数广义有序加权平均（C-GOWA）算子基础上,将IOWA算子和C-GOWA算子相结合,提出诱导有序加权连续区间广义有序加权平均（IOWC-GOWA）算子的概念。以指数支撑度作为相关性指标,构建新的组合预测模型。通过实例分析说明该模型能有效提高预测精度。     

Using Induced Ordered Weighted Averaging (IOWA) Operators for Aggregation in Cross‐Efficiency Evaluations

This paper proposes an enhancement of the cross‐efficiency evaluation through the aggregation of cross‐efficiencies by using a particular type of induced ordered weighted averaging (IOWA) operator. The use of a weighted average of cross‐efficiencies for the calculation of the cross‐efficiency scores, instead of the usual arithmetic mean, allows us to introduce some flexibility into the analysis. In particular, the main purpose of the approach we present is to provide aggregation weights that reflect the decision maker (DM) preferences regarding the relative importance that should be attached to the cross‐efficiencies provided by the different decision‐making units. To do it, an ordering is to be induced in the rows of the matrix of cross‐efficiencies, so the IOWA operator weights can be attached accordingly to the elements in each of the columns. The DM can thus incorporate his/her preferences by means of the choice of the variable that induces such ordering, and he/she may also adjust the importance to be attached to the most preferred cross‐efficiencies with the level of orness that is used when the aggregation weights are obtained.     

On the relation between Compromise Programming and Ordered Weighted Averaging operator

This paper will demonstrate a strong relationship between the Compromise Programming (CP) method and the Ordered Weighted Averaging (OWA) operator. We will mathematically prove that the application of the CP method reflects the pessimistic view of the Decision Maker (DM), if the distance from the ideal point is minimized. It will however present an optimistic DM, if the distance from the nadir is maximized. The OWA operator may cover all of the optimistic, neutral and pessimistic views.A practical relationship will be developed between the power value of the CP method and the optimism degree of the DM based on fuzzy quantifiers. The theoretical results will be illustrated by a real case study of a water resources management problem.     

Intuitionistic Fuzzy Interval‐Valued Linguistic Entropic Combined Weighted Averaging Operator for Linguistic Group Decision Making

Entropy, a basic concept of measuring the amount of information and the degree of confusion, has been applied in many weighted averaging operators in the linguistic group decision making. In the paper, we construct an intuitionistic fuzzy linguistic entropy based on the intuitionistic fuzzy entropy and the intuitionistic fuzzy linguistic variable. Then, inspired by operations of concentration and dilation (De SK, Biswas R, and Roy AR, Fuzzy Sets Syst. 2000;114(3):477?484), we extend the intuitionistic fuzzy linguistic entropy to the intuitionistic fuzzy interval‐valued linguistic entropy. After that, the intuitionistic fuzzy interval‐valued linguistic entropic combined weighted averaging (IFIVLECWA) operator is proposed for multiple attribute linguistic group decision making. Finally, a numerical example about the selection of optimal alternative(s) is presented to illustrate the applicability and effectiveness of the proposed method.     

基于容差关系的加权知识粗糙熵

粗集理论为从信息论角度研究知识粗糙熵和属性约简问题提供了一种重要的途径和方法。本文提出了基于容差关系下的不完备信息系统加权的知识熵和条件熵概念，将等价关系下的粗糙熵自然地推广到不完备信息系统的容差关系情形。本文的结果为在一般二元关系下的知识获取提供了理论依据。     

带权重条件嫡的属性约简算法

粗糙集理论中最重要的内容之一就是属性约简问题,现有的许多属性约简算法往往是基于属性对分类的重要性,如果属性约简的结果能满足用户实际需要的信息,如成本、用户的偏好等,那么约简理论将会有更高的实用价值。基于此,从信息熵的角度定义了带权重的属性重要性,然后重新定义了基于带权重的属性重要性的熵约简算法。最后通过实际例子说明,与基于属性重要性的嫡约简算法相比,考虑权重的算法更加符合用户的实际需求。     

基于相对信息熵和语言加权算子的网络舆情突发事件模糊多指标应急决策方法

在仔细分析网络舆情突发事件的成因与应急决策特性的基础上,从舆情状态、突发事件本身和民众关注3方面构建 网络舆情突发事件的属性指标。考虑到网络舆情突发事件指标的语言不确定性和多样性,以及应急专家对舆情事件危机评价的语言判断矩阵差异性,本文提出一个基于相对信息熵和语言加权算子的网络舆情突发事件模糊多指标群决策模型。首先根据应急指标重要性的判断矩阵,计算出每个专家对各指标权重的评价;然后基于相对熵的多属性组合赋权方法来获取应急专家的权重,计算得出各舆情事件指标的综合权重;再根据各应急专家对网络舆情事件的语言指 标评价值,利用语言加权算术平均算子,集结得到各网络舆情突发事件的综合决策评价值,进而对各舆情事件危机程度快速排序。最后,通过一个实例来证明本文方法的实用性。     

基于加权欧式算子的射频识别定位算法

基于有源射频识别校验的动态定位技术中边界标签的定位精度受制于参考标签数量,针对该问题,提出一种优化测距的改进算法。结合路径损耗模型,分析环境噪声对阅读器收信场强的可信度影响,通过引入加权算子调整定位标签与参考标签之间的欧氏距离,修正各阅读器上的收信场强可信度。仿真结果表明,对于检测区域边界的待定位标签,改进算法比原算法具有更好的定位精度及环境适应性。     

一种基于映射图像子块的图像缩小加权平均算法

针对一般缩小算法随着缩小比例降低,图像信息丢失愈发严重的现象,提出了一种基于映射图像子块的图像缩小加权平均算法.首先,根据缩小比例,求出缩小图像中每点映射在原始图像中的子块,然后,根据各子块,加权平均算出缩小图像中每点的像素值,从而达到缩小的目的.缩小后的图像具有较好的完整性,且算法简单、快速有效.实验结果表明,该算法与其他缩小算法相比,在保持图像完整性方面具一定的特点.最后,针对缩小后图像对比度偏低这一现象,提出了一种灰度拉伸的处理方法,并进行了实验对比,取得了较好效果.     

基于信息熵加权的局部离群点检测算法

离群点检测是数据挖掘领域的重要研究方向之一,可以从大量数据中发现少量与多数数据有明显区别的数据对象。在诸如网络入侵、无线传感器网络异常事件等检测应用中,离群点检测是一项具有很高应用价值的技术。为了提高离群点检测准确度,文中在局部离群测度（SLOM）算法的基础上,作了一些改进,提出了一种基于密度的局部离群点检测算法ESLOM。引入信息熵确定数据对象的离群属性,并对对象距离采用加权距离,以提高离群点检测准确度。理论分析和实验表明该算法是可行有效的。     

基于Teager算子的短时HRV样本熵算法

在心率变异性(HRV)数据的短时非线性分析中,单纯的样本熵算法不能有效提取健康人和充血性心衰(CHF)患者的信号特征差异。为此,提出一种基于Teager 能量算子的样本熵分析算法。采用Teager算子的预处理捕获心脏动力学活动中的异常节律变化,以强化样本熵分析效果。对MIT-BIH数据库中样本的实验结果表明,该算法可反映健康人与CHF患者的短时HRV信号非线性复杂性的差异,为计算机分析诊断心衰等疾病提供新的辅助依据。     

基于多尺度加权排列熵的管道泄漏检测

管道的泄漏检测对于物料长距离运输的安全至关重要。利用加权排列熵方法分析管道的压力时间序列,可提取压力时间序列的特征,通过判定所提取特征的变化,实现管道的泄漏检测。考虑到单尺度加权排列熵在反映压力信号复杂度方面的不足,提出了基于多尺度加权排列熵的管道泄漏检测方法。该方法采取移动窗口法,选取固定长度的压力序列作为子序列。计算该子序列的多尺度加权排列熵,从而判定管道的泄漏。最后,通过对管道泄漏实验装置的仿真试验,验证了所提算法的有效性。