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针对不确定多属性决策中的属性信息分布不均匀,且评价信息多数为二维信息的情况,本文提出了二维区间密度加权算子(TDIDW算子)的属性信息集结方法.依据密度算子的集结过程特点,文章首先定义了二维区间密度加权算子及其合成算子,然后介绍了基于灰色区间聚类法的评价信息分组方法以及基于非线性模型的密度加权向量确定方法,最后进行了算例验证.验证结果表明,该方法可以有效地解决由于属性信息分布不均匀而导致评价结果不准确的问题. 相似文献
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针对指标数据已知,而权重数据未知的群组赋权问题,给出了一种基于改进的区间数密度集结算子来进行指标群组赋权的决策方法。首先给出了区间数和区间数密度集结算子(IDM)的定义及性质,改进了以前区间数聚类的方法,应用直接法对一维区间数据组进行聚类,并定义了模糊统计量,以确定最为合理的一种聚类方式。然后基于改进的区间数密度集结算子这种数学模型,来解决指标值数据已知,而权重未知的群组赋权问题。最后举例说明该方法的可行性和实用性。 相似文献
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公路网评价对于了解区域公路网状态和交通需求间的关系非常重要.讨论了公路网属性表示为区间数的问题,通过计算属性值相对于属性等级的距离矩阵得到相应的加权可变相似度,再对可变相似度进行归一化处理,作为各属性等级的权重向量,同时应用级别特征公式求得综合属性值进行综合评价.实例表明,该方法和其他方法的评价结果一致,且能给出不同标准下的评价结果,计算过程简明且有效,而且能向属性值为其他类型的不确定量进行拓广. 相似文献
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结合实例说明相关文献中具有奖励作用的密度算子不能有效提升综合评价值,为此,分析原因并改进算子的密度权重的确定方法.进一步,将改进后的算子推广至犹豫模糊环境,在给定奖励等级基础上,确定等级系数、指标权重和密度权重,进而提出具有奖励作用的犹豫模糊密度算子,并研究其性质.然后,给出基于具有奖励作用的犹豫模糊密度算子的多属性决... 相似文献
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针对综合评价信息不完整、分布不均匀以及现实中人们总是主观性地经常“向后看”这一问题,提出了基于区间数有序加权平均算子(IOWA算子)的欧式范数综合评价方法。本文首先介绍了IOWA算子的相关知识;然后依据IOWA算子的特点,运用正态分布确定其位置加权向量,并与欧式范数结合形成加权欧式范数;最后运用一个算例验证了方法的有效性,既能充分考虑评价信息的分布情况,又使得评价更加客观准确。 相似文献
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一种基于决策者风险态度的区间数多指标决策方法 总被引:10,自引:2,他引:8
针对具有区间数的多指标决策问题,提出了一种新的决策分析方法。该方法的思路是:首先通过引入决策的风险态度因子将区间数决策问题映射为传统的点值决策问题。然后给出了基于TOPSIS的方案排序方法,最后通过对风险态度因子的不同取值可进行方案排序的灵敏度分析。 相似文献
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本文提出一种允许并列排名的组合评价方法,利用多种单一评价方法对方案进行排序,提取多个排序结果中的一致优劣关系作为约束条件,并以原始决策矩阵为自变量构造效用函数,通过目标规划求解各方案的综合效用值,以综合效用值的大小确定各方案的排名,综合效用值相同的方案被认为没有差别,排名并列。定义Weak-Kendall系数描述评价方法的鲁棒性,从数据的随机扰动和方案数量的变化两个角度设计仿真实验,引入实例,并与离差最大化法、均值法、模糊Borda法等组合评价方法进行比较,结果表明并列排名组合评价方法在这以上两方面具有更强的鲁棒性。最后,通过研究单一评价方法的选择对并列排名法的影响,探讨方法的改进方向。 相似文献
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为尽可能地规避医疗纠纷风险,通过专家群体评判医疗机构存在的医患之间潜在风险,以期到达控制风险的端口前移.建立了拓展后的空间Steiner点为专家群体最优集结模型,并应用PGSA方法对其进行求解,以获取最优集结点.将集结点值表示成由等级值si和距离等级最近的偏差αi所构成的二元语义(si,αi).通过线性加权得到待评医疗机构的评价值,并进行排序.在算例分析中,完整地运用了该论文提出的方法,得出了最优集结点及评判结果,从而破解了多属性空间专家群体最优集结的难题,为医疗纠纷风险评判问题的研究,提供了一种新的方法. 相似文献
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System reliability analysis involving correlated random variables is challenging because the failure probability cannot be uniquely determined under the given probability information. This paper proposes a system reliability evaluation method based on non-parametric copulas. The approximated joint probability distribution satisfying the constraints specified by correlations has the maximal relative entropy with respect to the joint probability distribution of independent random variables. Thus the reliability evaluation is unbiased from the perspective of information theory. The estimation of the non-parametric copula parameters from Pearson linear correlation, Spearman rank correlation, and Kendall rank correlation are provided, respectively. The approximated maximum entropy distribution is then integrated with the first and second order system reliability method. Four examples are adopted to illustrate the accuracy and efficiency of the proposed method. It is found that traditional system reliability method encodes excessive dependence information for correlated random variables and the estimated failure probability can be significantly biased. 相似文献
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基于区间数贴近度的不确定多属性决策模型 总被引:1,自引:0,他引:1
针对只有部分权重信息且属性值以区间数形式给出的多属性决策问题,提出了一种基于区间数贴近度的决策方法.首先讨论了区间数贴近度的定义和性质;然后给出了解决不确定多属性决策问题的一般步骤.并依据传统的逼近理想解的基本思路,以实际评价值与理想解之间的贴近度最大化为目标建立优化模型,从而得到指标权重.进而计算出每个方案与正理想解的相对贴近度,即可得到所有方案的排序结果.方法能充分利用规范化评价的先验信息,评价结果客观可靠,不具有主观随意性.最后通过实例分析验证了该方法的有效性和实用性. 相似文献
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Farhad Hosseinzadeh Lotfi Mohsen Rostamy-Malkhalifeh Nazila Aghayi Zahra Ghelej Beigi Kobra Gholami 《Applied Mathematical Modelling》2013,37(1-2):25-33
Scoring rules are an important disputable subject in data envelopment analysis (DEA). Various organizations use voting systems whose main object is to rank alternatives. In these methods, the ranks of alternatives are obtained by their associated weights. The method for determining the ranks of alternatives by their weights is an important issue. This problem has been the subject at hand of some authors. We suggest a three-stage method for the ranking of alternatives. In the first stage, the rank position of each alternative is computed based on the best and worst weights in the optimistic and pessimistic cases, respectively. The vector of weights obtained in the first stage is not a singleton. Hence, to deal with this problem, a secondary goal is used in the second stage. In the third stage of our method, the ranks of the alternatives approach the optimistic or pessimistic case. It is mentionable that the model proposed in the third stage is a multi-criteria decision making (MCDM) model and there are several methods for solving it; we use the weighted sum method in this paper. The model is solved by mixed integer programming. Also, we obtain an interval for the rank of each alternative. We present two models on the basis of the average of ranks in the optimistic and pessimistic cases. The aim of these models is to compute the rank by common weights. 相似文献
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针对时序动态综合评价中多阶段信息集结问题,提出一种体现发展趋势的自-互激励型集结方法。首先,根据被评价对象不同的发展趋势得出不同的预测点,连接预测点得到预测线,以预测线为基准对被评价对象进行状态上的自激励;其次,依据原始评价值的大小衡量增长难度,并与增长速度结合,进行增长水平上的互激励;最后,通过算例验证方法的有效性。该集结方法从激励的视角考虑被评价对象自身的发展趋势和相对增长水平,据此对不同起点的对象实行差别化激励,充分尊重了个体发展差异。 相似文献
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Kevin Leckey Ralph Neininger Henning Sulzbach 《Stochastic Processes and their Applications》2019,129(2):507-538
A fundamental algorithm for selecting ranks from a finite subset of an ordered set is Radix Selection. This algorithm requires the data to be given as strings of symbols over an ordered alphabet, e.g., binary expansions of real numbers. Its complexity is measured by the number of symbols that have to be read. In this paper the model of independent data identically generated from a Markov chain is considered.The complexity is studied as a stochastic process indexed by the set of infinite strings over the given alphabet. The orders of mean and variance of the complexity and, after normalization, a limit theorem with a centered Gaussian process as limit are derived. This implies an analysis for two standard models for the ranks: uniformly chosen ranks, also called grand averages, and the worst case rank complexities which are of interest in computer science.For uniform data and the asymmetric Bernoulli model (i.e. memoryless sources), we also find weak convergence for the normalized process of complexities when indexed by the ranks while for more general Markov sources these processes are not tight under the standard normalizations. 相似文献
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By modifying the method of projection, the results of Hajek and Huskova are extended to show the asymptotic normality of signed and linear rank statistics under general alternatives for dependent random variables that can be expressed as independent vectors of fixed equal length. The score function is twice differentiable; the regression constants are arbitrary; and the distribution functions are continuous, but arbitrary. As an application, a rank transform statistic is proposed for the one-sample multivariate location model. The ranks of the absolute values of the observations are calculated without regard to component membership, and the scored ranks are substituted in place of the observed values. The limiting distribution of the proposed test statistic is shown to be χ2 divided by the degrees of freedom under the null hypothesis, and noncentral χ2 divided by the degrees of freedom under the sequence of Pitman alternatives. 相似文献
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《Applied Mathematical Modelling》2014,38(11-12):2969-2982
This paper presents a multiple attribute group decision making model based on aggregating crisp values into intuitionistic fuzzy numbers. First, each alternative is evaluated with respect to their attributes, whose values are provided by decision maker as crisp numbers. Second, to make a reasonable normalization of attribute values in the group decision making environment, a maximum grade and a minimum grade are added to the attribute values. These normalized attribute values are then aggregated (per attribute) into an induced intuitionistic fuzzy number. Each alternative is then evaluated according to the induced intuitionistic fuzzy number. To show the major technical advances in this paper, comparisons with other methods are also made. Finally, an experimental analysis for supplier selection is given to illustrate the reasonableness and efficiency of the introduced method. 相似文献