萌说新语——MATLAB入门学习15

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入门学习15

犹豫模糊双层次权重确定方法

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MATLAB学习——

犹豫模糊双层次权重确定方法，

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Today, the theme I bring to you is

MATLAB learning——

Hesitant fuzzy two-level weight determination method,

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编码详解

现建立规格为4×5，且权重完全未知、属性值为犹豫模糊集的决策矩阵，对应在MATLAB中显示的代码为：

Now establish a decision matrix with a size of 4 × 5, the weights are completely unknown, and the attribute values are hesitant fuzzy sets. The corresponding code displayed in MATLAB is:

新的技能!

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。

顺便我们需要测量一下尺寸，不要忘记噢~

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.

By the way, we need to measure the size, don’t forget~

方案层面优化模型

方案层面考虑两个因素——方差和记分函数。方差V代表犹豫模糊集内元素的分散程度，值越高元素分布越不集中；记分函数G代表方案可靠程度，值越高方案越优。综合考虑，建立如下优化模型M1:

Two factors are considered at the program level – variance and scoring function. The variance V represents the dispersion degree of elements in the hesitant fuzzy set, and the higher the value, the less concentrated the distribution of elements; the scoring function G represents the reliability of the scheme, and the higher the value, the better the scheme. Comprehensive consideration, the following optimization model M1 is established:

我们要将代码输入到我们的MATLAB中：

从而运行的到：

以上就是我们所求的犹豫模糊集的方差。我们的delta通常来说=0.1

The above is the variance of the hesitant fuzzy set we seek. Our delta is usually 0.1

基于此，我们是可以在MATLAB中得出积分函数的：

Based on this, we can derive the integral function in MATLAB:

之后我们需要用：

S_Membership=V_Membership./(V_Membership+G_Membership)

这个代码求出方差和得分函数的分散率：

得到结果：

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