不同属性值下的标准VIKOR方法代码实现
Standard VIKOR method code implementation under different attribute values
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今天小编为大家带来的主题是
MATLAB学习——直觉模糊集，
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Dear you, this is LearningYard Academy.
Today, the theme I bring to you is
MATLAB learning - triangular fuzzy numbers,
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This tweet is about 5 minutes long to read.
Please read with patience.
“
上周我们进行三角模糊数后半段的代码的学习，今日小编带大家学习新的领域吧！
Last week, we learned the basic concepts of triangular fuzzy numbers. Today, Xiaobian will take you to learn the part of the detailed code!
“
直觉模糊集
直觉模糊决策是基于直觉模糊信息的决策问题，直觉模糊集是Atanassov教授最早于1983年提出的一种模糊信息的概念，把只考虑隶属度的Zadeh 经典模糊推广为同时考虑真隶属度、假隶属度和犹豫度这三方面信息的直觉模糊集。
Intuitive fuzzy decision-making is a decision-making problem based on intuitionistic fuzzy information. Intuitive fuzzy set is a concept of fuzzy information first proposed by Professor Atanassov in 1983. Zadeh's classical fuzzy that only considers membership is extended to consider both true membership and false membership. Intuitive fuzzy sets of information on the three aspects of degree and hesitation degree.
其定义为：
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基本运算法则
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基本代码介绍：
首先我们现建立规格为4×5，且权重已知、属性值为直觉模糊集的决策矩阵，见下面评价矩阵表格，其中C1、C2、C3为效益型指标，C4与C5为成本型指标。
First, we now establish a decision matrix with a size of 4×5, with known weights and attribute values of intuitionistic fuzzy sets. See the evaluation matrix table below, in which C1, C2, and C3 are benefit-type indicators, and C4 and C5 are cost-type indicators.
C1 | C2 | C3 | C4 | C5 | |
ω | 0.1 | 0.2 | 0.3 | 0.1 | 0.3 |
A1 | [0.6,0.2] | [0.4,0.3] | [0.8,0.6] | [0.9,0.3] | [0.6,0.3] |
A2 | [0.8,0.3] | [0.9,0.2] | [0.7,0.4] | [0.8,0.3] | [0.6,0.5] |
A3 | [0.4,0.2] | [0.7,0.3] | [0.6,0.4] | [0.8,0.3] | [0.7,0.4] |
A4 | [0.3,0.4] | [0.6,0.5] | [0.7,0.4] | [0.7,0.4] | [0.8,0.4] |
按指标类型进行排列，效益性指标在前，成本型指标在后。找到第几列开始为成本型指标。Cost_Column在对for循环内定义列的大小有很大的作用，无论矩阵列数怎样变化都可以正确实现自动化操作。输入权重的同时顺便测量一下原初矩阵的尺寸：
Arranged by index type, with the benefit index first and the cost index last. Find the first few columns to start with cost-type indicators. Cost_Column plays a big role in defining the size of the column within the for loop, and automation can be performed correctly no matter how the number of matrix columns changes. While entering the weights, measure the size of the original matrix by the way:
这之后将成本型指标转化为效益型指标：
之后我们需要分别找到每列隶属度和非隶属度的最大最小值，并将其进行组合：
Then we need to find the maximum and minimum values of membership and non-membership for each column separately, and combine them:
于是我们可以得到如下结果：
So we can get the following results:
图：直觉模糊决策矩阵最大最小值
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