TOPSIS matlab代码

以下是一个使用MATLAB实现TOPSIS方法的代码示例：

% 原始决策矩阵
X = [89 1; 60 3; 74 2; 99 0];

% 标准化决策矩阵
normalized_X = X ./ repmat(sqrt(sum(X .* X)), size(X, 1), 1);

% 权重向量
weights = [0.5, 0.5];

% 正理想解和负理想解
pos_ideal_solution = max(normalized_X);
neg_ideal_solution = min(normalized_X);

% 计算每个方案到正理想解的距离
dist_pos_ideal = sqrt(sum((normalized_X - repmat(pos_ideal_solution, size(normalized_X, 1), 1)).^2, 2));

% 计算每个方案到负理想解的距离
dist_neg_ideal = sqrt(sum((normalized_X - repmat(neg_ideal_solution, size(normalized_X, 1), 1)).^2, 2));

% 计算综合评价指数
performance_score = dist_neg_ideal ./ (dist_pos_ideal + dist_neg_ideal);

% 根据综合评价指数排序
 = sort(performance_score, 'descend');

% 输出排序结果
rank

这段代码首先将原始决策矩阵进行标准化，然后计算每个方案到正理想解和负理想解的距离，接着根据综合评价指数对方案进行排序，得到最终的排名结果。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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