模糊层次分析法matlab,求三角模糊数层次分析法(FEAHP)模型计算的MATLAB程

以下是三角模糊数层次分析法(FEAHP)模型计算的MATLAB程序示例：

clc;
clear;

% Define the fuzzy decision matrix
D = [
    [1, 0.2, 0.4, 0.3];
    [5, 1, 0.5, 0.2];
    [2.5, 2, 1, 0.6];
    [3.3, 5, 1.7, 1];
];

% Define the triangular fuzzy sets
L = [0, 0, 0.5];
M = [0, 0.5, 1];
H = [0.5, 1, 1];

% Define the weights of the criteria
w = [0.4, 0.25, 0.2, 0.15];

% Calculate the normalized decision matrix
[n, m] = size(D);
D_n = zeros(n, m);
for i = 1:n
    row_sum = sum(D(i, :));
    for j = 1:m
        D_n(i, j) = D(i, j) / row_sum;
    end
end

% Calculate the weighted normalized decision matrix
D_w = zeros(n, m);
for j = 1:m
    for i = 1:n
        D_w(i, j) = D_n(i, j) * w(j);
    end
end

% Calculate the fuzzy synthetic evaluation vector
V = zeros(1, n);
for i = 1:n
    L_i = D_w(i, :) * L';
    M_i = D_w(i, :) * M';
    H_i = D_w(i, :) * H';
    V(i) = (L_i + 2*M_i + H_i) / 4;
end

% Normalize the fuzzy synthetic evaluation vector
sum_V = sum(V);
V_n = V / sum_V;

% Print the results
disp('The normalized fuzzy synthetic evaluation vector is:');
disp(V_n);

在上面的程序中，我们首先定义了模糊决策矩阵D、三角模糊集L、M和H，以及每个标准的权重w。接下来，我们计算了规范化决策矩阵D_n和加权规范化决策矩阵D_w。然后，我们使用三角模糊数计算了模糊综合评价向量V，并将其规范化以得到最终评价向量V_n。最后，我们输出规范化模糊综合评价向量。