Multiperiod multiattribute decision‐making method based on trend incentive coefficient

In this paper, we develop a method for multiperiod multiattribute decision‐making (MP‐MADM) problems, in which the decision information, including attribute weights and attribute values, is given at different periods. First, using the variation in attribute values of the various alternatives for unit time, we can obtain the trend incentive coefficient of variation that represents reward or punishment for the development tendency of alternatives. This paper proposes a method based on maximum entropy ordered weighted averaging (MEOWA) to determine the trend incentive coefficient. Second, considering the differences development tendency of the alternatives, we propose an approach that integrates the trend incentive coefficient and the original decision information to solve the MP‐MADM problems. Finally, two MP‐MADM cases are used to illustrate the effectiveness and practicability of the proposed method. Comparisons with previous research are also discussed.     

Dynamic fuzzy OWA model for group multiple criteria decision making

Obtaining relative weights in MCDM problems is a very important issue. The Ordered Weighted Averaging (OWA) aggregation operators have been extensively adopted to assign the relative weights of numerous criteria. However, previous aggregation operators (including OWA) are independent of aggregation situations. To solve the problem, this study proposes a new aggregation model – dynamic fuzzy OWA based on situation model, which can modify the associated dynamic weight based on the aggregation situation and can work like a “magnifying lens” to enlarge the most important attribute dependent on minimal information, or can obtain equal attribute weights based on maximal information. Two examples are adopted in this paper for comparison and showing the effects under different weights.     

The use of ordered weighted averaging method for decision making under uncertainty

A decision making under uncertainty (DMUU) prevails at the outset and often evolves into a decision making under partial uncertainty as information on the states of nature, for example, a probability distribution, is advanced. Many methods have emerged for solving the DMUU problems, which includes the classical decision criteria and the domain criterion. Yager (1988) introduced a new approach, the so‐called ordered weighted averaging (OWA) as a viable method for solving the DMUU problems. The OWA weights to be used in the aggregation are generated under the degree of optimism provided by a decision maker and then combined with the reordered payoffs to produce aggregated payoffs for each strategy. The reordering process, one of the characterizing features of the OWA method, enables us to perform various types of aggregations including maximax, maximin, and Hurwicz‐α index in conjunction with the generated weights. The OWA method obviously extends the Hurwicz approach by taking into account the tradeoffs among the entire payoffs while the Hurwicz approach considers a tradeoff only between the two extremes, the maximum and the minimum payoffs. In this paper, we examine the features of the OWA method in light of Milnor's set of requirements for reasonable decision criteria, thus providing a solid methodological foundation for the DMUU. The OWA method can also be used to solve a group DMUU problem by exploiting individual decision results in the situation when the use of a fuzzy majority is advocated.     

基于直觉模糊熵的专家权重确定方法及其验证

针对专家判断信息以直觉模糊集给出的直觉模糊群决策矩阵,提出一种新的客观确定专家权重的方法。与传统的通过专家评价的差异程度来确定专家权重的思路不同,该方法通过定义直觉模糊集的模糊熵计算专家判断信息的模糊程度,进而确定每位专家的权重,并对基于犹豫度、几何距离、相似度量和不确定程度4类模糊熵的定义对专家权重结果的影响进行实验和仿真分析。仿真结果表明,专家的权重不仅取决于不同类模糊熵的定义,还与专家个数和属性个数相关。     

An argument‐dependent approach to determining OWA operator weights based on the rule of maximum entropy

The methods for determining OWA operator weights have aroused wide attention. We first review the main existing methods for determining OWA operator weights. We next introduce the principle of maximum entropy for setting up probability distributions on the basis of partial knowledge and prove that Xu's normal distribution‐based method obeys the principle of maximum entropy. Finally, we propose an argument‐dependent approach based on normal distribution, which assigns very low weights to these “false” or “biased” opinions and can relieve the influence of the unfair arguments. A numerical example is provided to illustrate the application of the proposed approach. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 209–221, 2007.     

基于熵权Vague集的多目标决策方法

针对目前基于Vague集的多目标决策中目标权重的主观任意性以及评价函数的单一性问题,提出一种基于熵权Vague集的多目标决策方法。首先,将决策矩阵转化为目标优属度矩阵,再利用熵权系数法计算各个目标的客观权重,综合考虑客观权重和主观权重得到各个目标的权重向量区间;然后,通过计算方案的支持目标集、反对目标集和中立目标集得出方案的Vague估计值;最后,定义新的评价函数对方案进行排序并选出最优方案。通过算例验证了所提方法的合理性和有效性。     

Behavioral ordered weighted averaging operator and the application in multiattribute decision making

This paper introduces a new type of behavioral ordered weighted averaging (BOWA) operator, to incorporate decision maker’s gains and losses behavior tendency into the information aggregation process. The main characteristic of this BOWA operator is that it considers behavioral weights and ordered weights in the same formulation. We further provide a calculation method of the behavioral weights, in which various psychological preferences of different attribute types of the decision maker can be expressed intuitively. In addition, we discuss some particular cases of BOWA operator and its main properties. Finally, a numerical example is used to illustrate the use of the proposed method.     

基于核密度最大熵方法的杂系混合信号盲分离*

传统的独立分量分析方法普遍存在的非线性评价函数只能凭经验选取,当混合信号同时包含超高斯和亚高斯信号时,算法难以取得很好的分离效果。利用基于随机变量矩的核密度最大熵方法对非线性函数进行直接估计,提出了基于核密度最大熵方法的杂系混合信号盲分离算法,成功地分离了杂系混合信号。仿真结果验证了算法的有效性。     

On some characteristics and related properties for OWF and RIM quantifier

Regular Increasing Monotone (RIM) quantifier and Ordered Weighted Function are important counterparts of discrete ordered weighted averaging operators. Some important characteristics such as entropy, Moment, and Step/Hurwicz degree have already been proposed and studied by several researchers. The main propose of this paper is to put the concepts of entropy, Moment, and Step/Hurwicz degree for RIM quantifier into a continuous environment. Some well‐defined representative families of RIM quantifiers are also presented. The metric spaces of RIM quantifiers are discussed.     

基于灰关联深度系数的评价指标客观权重极大熵配置模型

在多属性决策领域中,经典评价指标客观赋权的极大熵模型主要依据指标评价值的差异度进行赋权,极易导致对评价值差异度小的指标作出不重要(权值小)的错误判断,从而产生决策偏差.对此,首先提出灰关联深度系数的概念,以表征指标客观权重包含的信息量大小;同时运用极大熵理论建立评价指标客观权重的极大熵配置模型,以确定多属性决策指标权重,较好地解决了经典模型存在的严重缺陷;最后通过实际案例的对比分析,验证了所提出解决方案的优良性能.     

基于量子遗传算法的二维最大熵图像分割

图像分割二维最大熵算法存在计算复杂度高的弊端,目前针对这个问题所提出的各类算法效果都不太理想。依据量子遗传算法种群多样性好、收敛速度快的特点,提出了一种基于量子遗传算法的二维最大熵算法,与基于标准遗传算法的二维最大熵算法相比较,取得了更好的实验效果。     

基于最大熵模型的中文指代消解系统实现*

实现了一个基于最大熵的中文指代消解系统。通过预处理获得相关信息,抽取出12特征,采用最大熵算法训练生成分类器。在ACE05 bnews中文测试语料上的指代消解实验结果表明,本系统是一个中文指代消解研究的较好平台。     

基于熵权的区间数多属性决策方法

研究了属性权重未知且属性值为区间数的多属性决策问题。首先针对一般区间数排序方法完全依赖区间数中点值的缺陷,提出了一种改进的排序方法;接着给出了改进的区间数判断矩阵规范化方法;然后依据多属性决策中传统熵权确定方法的思路,结合区间数决策矩阵的特点提出了一种确定属性熵权的方法;最后通过一个算例说明了该方法的实用性和有效性。     

Z‐uncertain probabilistic linguistic variables and its application in emergency decision making for treatment of COVID‐19 patients

Probabilistic linguistic term sets (PLTSs) have many applications in the field of group decision making (GDM) because it includes both linguistic evaluation and probabilistic distribution when expressing preference information. However, the difference of information credibility in PLTSs is ignored, resulting in an inaccurate representation of decision information and unreasonable probability calculation. In this paper, we first consider the credibility of the information and propose the concept of Z‐uncertain probabilistic linguistic variables (Z‐UPLVs). Subsequently, the operational rules, normalization, distance and similarity measures, and comparison method of Z‐UPLVs are introduced. Then, a probability calculation method based on credibility, an extended TOPSIS method, and some operators are proposed, which can be applied to emergency decision making in the Z‐uncertain probabilistic linguistic (Z‐UPL) environment. Finally, an emergency decision‐making case of COVID‐19 patients and comparative analysis illustrate the necessity and effectiveness of this method.     

应用最大熵和思维进化算法的阈值分割

在指数熵的基础上给出了模糊指数信息熵的定义及其性质,避免了对数中无定义点的问题,并用此概念和条件概率定义图像模糊划分的熵,根据熵最大原理进行图像自动分割。为了降低计算复杂度,提高计算速度,改进了思维进化算法（MEA）,设计了自适应趋同和小概率随机异化操作,优化模糊隶属参数,搜索最优分割阈值。实验结果表明,该方法能够自动、有效地选取阈值,分割效果优于Otsu等其他算法,并能保留原始图像的主要特征。     

基于分散微粒群算法的二维模糊最大熵图像分割

该文研究了基于二维模糊信息熵的图像分割方法,针对二维模糊信息熵图像分割方法求取阈值时存在的计算复杂、时间长、实用性差等问题,提出了基于优化微粒群算法的二维最大熵图像分割方法。DPSO算法对图像的二维阈值空间进行全局搜索,并将搜索得到的二维熵最大值所对应的点灰度-区域灰度均值作为阈值进行图像分割。同时,为了避免该算法收敛到局部最优解的问题,在算法中引入了变异策略。通过实验显示了该算法在收敛性和计算效率上较QPSO在内其它优化算法具有更好的优越性。     

模糊最大熵多阈值分割的改进算法研究

基于模糊最大熵原则的多阈值分割,提出了遗传算法和ICM相结合的改进算法。该方法首先确定选取模糊熵函数作为适应度函数,然后对遗传算法中的编码方式、交叉算子、变异算子等参数进行了一些适当改进,进而给出了该算法的理论推导和算法的具体实现步骤。与通常的基于模糊最大熵原理进行阈值分割方法相比较,减少了计算量并且提高了运行效率,克服了常用方法在阈值求取时的一些不足,能够快速获得稳定的阈值。对比实验得出的结果,也说明了该方法的快速性、有效性、稳定性。     

基于改进模糊熵和证据推理的多属性决策方法

针对证据推理方法框架下属性权重难以获取的问题,提出一种基于改进模糊熵和证据推理的多属性决策方法。首先,定义证据推理信度决策矩阵框架下的三角函数模糊熵公式,并证明了其满足熵的四个公理化定义。其次,所提方法能够同时处理属性权重完全未知和属性权重信息部分已知两种情况：当属性权重完全未知时,基于信度框架下的改进模糊熵和熵权法的基本思想计算属性权重;当属性权重信息部分已知时,定义加权模糊熵,建立期望模糊熵最小的线性规划模型求解最优属性权重。最后,利用证据推理算法融合方案属性值,结合期望效用理论得到方案排序结果。通过实例计算,并与传统模糊熵计算方法进行比较分析,验证了所提方法能够更加充分地反映原始决策信息,更具客观性和一般性。     

A new DEA‐based method for fully ranking all decision‐making units

Abstract: Decision makers always lay great emphasis on performance evaluation upon a group of peer business units to pick out the best performer. Standard data envelopment analysis models can evaluate the relative efficiency of decision‐making units (DMUs) and distinguish efficient ones from inefficient ones. However, when there are more than one efficient DMU, it is impossible to rank all of them solely according to standard efficiency scores. In this paper, a new method for fully ranking all DMUs is proposed, which is based on the combination of each efficient DMU's influence on all the other DMUs and the standard efficiency scores. This method is effective in helping decision makers differentiate all units' performance thoroughly and select the best performer.     

基于灰度差分和二维最大熵阈值的新闻字幕检测*

针对新闻视频帧中文本区域的定位提取问题,提出了一种有效的字幕定位提取方法。通过灰度差分和变异灰度直方图对新闻视频帧字幕区域定位,再经改进的二维最大熵阈值方法对分割出的文字区域进行二值化,得到可识别的文字图片。最后对文本定位和OCR识别情况进行了算法对比。实验表明：与传统的投影法和最大熵方法相比,该方法可有效地提高文本定位的查全率和OCR的识别率。