决策矩阵

AHP评估在所有3个方面都完成了之后，应用程序比分可能被整理形成一个决策矩阵，其中一个样例如表5所示。

Once the AHP evaluation is done for all three dimensions , application scores can be collated to arrive at a decision matrix , a sample of which is shown in Table 5 .

实例计算的结果说明，直接由决策矩阵样本数据驱动的PPIPM模型用于动态多指标决策问题简便可行，适用性和可操作性强，具有推广应用价值。

The case study shows that applying PP-IPM model driven directly by decision matrix samples data is simple , feasible , general and practical , and PP-IPM model can be applied to many dynamic multiple attribute decision problems .

Fuzzy决策矩阵及应用

Fuzzy Decision-making Matrix and Its Application

在AHP的框架下，提供了指标权重和偏好值语言信息并用三角模糊数来描述，构造语言决策矩阵并进行模糊化。

First - ly , in the analytic hierarchy process the linguistic information on index weights and performance values are provided and presented as triangular fuzzy number . Secondly , the linguistic decision - making matrices are constructed and fuzzified .

最后由运筹专业人员利用TOPSIS法对行为集结的最后结果（决策矩阵）进行数学集结，较好地做到了决策在定性和定量上的综合集成。

In the end . operations research career men concentrate the final result of behaviors concentration ( decision-making matrix ), and preferably achieve synthetical integration of decision-making qualitatively and quantitatively by using of TOPSIS algorithm .

讨论了按指标分层决策矩阵的多指标综合评价问题，描述了按指标分层的决策矩阵的求解算法，并采用VISUALFOXPRO数据库语言编程实现。

In this paper we discussed the attribute divided layers addition weighting incorporate method of multiple attribute comprehensive evaluate system . We described the algorithm of addition weighting incorporate of decision matrix with attribute divided layers and edited programs with Visual Foxpro to realize it .

在AHP的框架下，通过三角模糊数表示元素之间的比较比率，定义了两个模糊数之间的距离，利用模糊综合扩展分析方法构造决策矩阵。

Firstly , the comparison ratios of elements ' importance are presented as triangular fuzzy numbers in the analytic hierarchy process and the distance between two triangular fuzzy numbers is defined . Secondly , a fuzzy decision-making matrix is constructed by using fuzzy synthetic extent analysis .

对决策矩阵进行规范化处理是MCDM决策的必要步骤，其目的是消除指标单位影响，使得不同指标的值具有可加性。

Normalizing decision matrix is one of the necessary steps for transacting the MCDM with the aim of eliminating the influence of the target 's unit , so that different targets ' expected values are additive .

论文在研究已有数据约简算法的基础上，提出了利用扩展决策矩阵计算属性最佳约简的完备的EDMAR算法。

Motivated by the former algorithms , in this paper , a novel algorithm based on extended decision matrices for attribute reduction ( EDMAR ) is proposed . Its completeness is proved .

研究决策矩阵的元素以模糊语言形式给出的多属性群决策问题，给出一种语言OWA算子。

In this paper , we study the multi attribute group decision making problems in which the elements in decision matrices are given in the forms of fuzzy linguistic assessments , and give a linguistic ordered weighted averaging ( OWA ) operator of the fuzzy linguistic assessments .

扩展了决策矩阵的定义，并在此基础上提出一种增量式规则提取算法（IREA），该算法能够以增量的方式从样本数据中提取确定性和可能性规则。

In this paper , the definition of decision matrices is extended , based on which an incremental rule extraction algorithm ( IREA ) is proposed . Certain rules and possible rules can be extracted in an incremental mode from sample data using IREA .

一种基于决策矩阵的属性约简及规则提取算法

Attribute reduction and rule extraction algorithms based on decision matrices

基于集对分析的方案评价决策矩阵与应用

An Applications of the Scheme-appraisal Decision Matrix Based on Set Pair Analysis

基于可能度的决策矩阵排序的一种新方法

A New for Priority of Decision-making Matrix Method Based on Possibility Degree

确定区间数决策矩阵属性权重的方法&熵值法

A method of entropy for obtaining the attribute weights of interval numbers decision-making matrix

总结归纳了雷达的有源遮盖式干扰的几种相关模型，并利用新的干扰效果评价指标构建了雷达干扰效益决策矩阵。

The radar jamming effective decision-making matrix is built by new interference effect evaluation indicators .

改进的决策矩阵及其属性约简方法

Improved Decision Matrix and Attribute Reduction

结论表明，几何平均综合矩阵的一致性指标要小于各决策矩阵一致性指标的算术平均数。

The conclusion indicated that the consistency of GMJ was less than the mean of the consistencies of judgments .

根据所提出的效用函数，将决策矩阵归一化到相应的效用矩阵。

In this paper , the set pair analysis is applied to build effectiveness function for multiple objective decision-making .

该算法利用了决策矩阵求值核，以属性重要度作为启发式构建了启发式值约简算法。

It used decision matrix drawing data core , used attribute importance as heuristic forming heuristic data reduction algorithm .

推广了决策矩阵的几种规范化方法及与此相应的理想和负（反）理想解（点）的确定法。

The method for normalizing decision matrix and determing the corresponding ideal and negative-ideal solutions ( points ) is generalized .

第二章作者提出了属性权重已知时决策矩阵的几种排序方法。

In the second chapter , the author puts forward several arrangement methods of decision-making matrix when attribute weights are known .

研究决策矩阵中元素以语言形式给出的多属性群决策问题。

In this paper , we study the group decision making problems in which the elements in decision matrices are linguistic evaluation labels .

介绍语言评估标度集，定义虚拟术语和虚拟术语指标等概念，给出与语言决策矩阵相对应的指标矩阵。

A set of linguistic evaluation scales is introduced , and the concepts of virtual term and virtual term index , etc , are defined .

然后提出了一种兼顾效益和成功率的区间数决策矩阵排序的综合评价方法，该方法能融合多方面的决策信息。

Then the synthetic ranking approach with the criterions of benefit and successful probability is presented , which can integrate decision-making information from many aspects .

通过规范化决策矩阵和目标权重向量得到各方案的综合评价值；

Furthermore , with the help of the normalized decision-making matrix and the object weight vector , the comprehensive evaluations of the alternatives were worked out .

分析了等价矩阵和联合决策矩阵规则提取算法对于大数据集低效性的根源。

Based on equivalence matrix and joint decision matrix , the reason of the existing algorithms inefficiency for rules extraction in massive data set is analyzed .

同时，借助决策矩阵进行值约简，提出了一种新的规则提取算法，使最终得到的决策规则更加简洁。

Furthermore , a new algorithm for rule extraction based on decision matrices was presented . And much more concise decision rules could be got with this method .

目前的区间数的决策矩阵排序方法都只根据效益型准则进行的，而没有考虑到方案实施的成功率这个重要准则。

The ranking methods of interval numbers decision-making matrix have been given by the criterions of benefit at present , but the criterions of alternatives successful probability are not considered .

该方法以原始数据样本与理想方案之间的灰色关联系数矩阵为新的决策矩阵，利用理想解法对方案进行排序。

First take the gray correlation coefficient matrix between primitive data sample and ideal scheme as new decision-making matrix , and then use TOPSIS to evaluate and arrange all schemes .