数据：data.txt

290 85 90 100 75 80 85 60 70 75 50 55 60

288 85 90 100 75 80 85 85 90 100 60 70 75

288 75 80 85 85 90 100 50 55 60 60 70 75

285 85 90 100 75 80 85 75 80 85 75 80 85

283 75 80 85 85 90 100 75 80 85 60 70 75

283 75 80 85 50 55 60 85 90 100 75 80 85

280 85 90 100 75 80 85 60 70 75 75 80 85

280 75 80 85 85 90 100 85 90 100 60 70 75

280 75 80 85 75 80 85 85 90 100 75 80 85

280 50 55 60 60 70 75 85 90 100 60 70 75

278 50 55 60 60 70 75 75 80 85 85 90 100

277 85 90 100 75 80 85 60 70 75 85 90 100

275 75 80 85 60 70 75 50 55 60 85 90 100

275 50 55 60 75 80 85 85 90 100 75 80 85

274 85 90 100 75 80 85 60 70 75 75 80 85

273 75 80 85 85 90 100 75 80 85 60 70 75

完整程序：

clc, clear

load data.txt

sj = [repmat(data(:,1),1,3), data(:,2:end)]; % 将第一列的成绩值也采用同样的方式扩展

% 此时数据一列为一个指标，一行为一个样本

% 归一化处理

n = size(sj,2)/3;

m = size(sj,1);

w = [0.5*ones(1,3), 0.125*ones(1,12)]; % 五项指标的权重向量(5x3)

w = repmat(w,m,1);

y = [];

for i = 1:n

tm = sj(:,3*i-2:3*i);

max_t = max(tm);

max_t = repmat(max_t, m, 1);

max_t = max_t(:,3:-1:1);

yt = tm./max_t;

yt(:,3) = min([yt(:,3)'; ones(1,m)]);

y = [y,yt];

end

% 求解模糊决策矩阵

r = [];

for i = 1:n

tm1 = y(:,3*i-2:3*i);

tm2 = w(:,3*i-2:3*i);

r = [r, tm1.*tm2];

end

% 求M+和M-的距离

m_plus = max(r);

m_minus = min(r);

d_plus = dist(m_plus,r');

d_minus = dist(m_minus,r');

% 求隶属度

mu = d_minus./(d_minus+d_plus);

[mu_sort, ind] = sort(mu, 'descend')