topsis综合评价法matlab代码
时间: 2024-10-09 07:11:58 浏览: 64
Topsis(Technique for Order of Preference by Similarity to Ideal Solution)是一种决策分析方法,它通过比较每个备选方案与理想解和最差解之间的相对优势来进行排序。在MATLAB中,你可以编写脚本来实现Topsis算法,以下是基本步骤:
1. **导入数据**:首先需要读取包含指标权重、方案得分和理想解(最小值)、最大值的数据矩阵。
```matlab
% 读取数据
data = readtable('your_data.csv'); % 替换为你的文件名
```
2. **计算正向化和负向化得分**:对于每一个方案和指标,如果该指标是目标,则正向化,反之则负向化。
```matlab
weights = data.Weight; % 权重向量
scores = data.Score;
pos_scores = scores + (weights - max(weights)) ./ abs(weights - max(weights)); % 正向化
neg_scores = scores - (weights - min(weights)) ./ abs(weights - min(weights)); % 负向化
```
3. **计算加权和**:对正向化和负向化得分分别进行加权求和。
```matlab
pos_sum = sum(pos_scores .* weights, 2);
neg_sum = sum(neg_scores .* weights, 2);
```
4. **计算合成得分**:将正向化和负向化得分转换为合成得分,通常取两者距离理想解的最小者作为总分。
```matlab
distances_pos = pos_sum - min(pos_sum, [], 'all');
distances_neg = neg_sum - min(neg_sum, [], 'all');
% 合成得分
total_score = distances_pos + distances_neg;
```
5. **确定最优和最劣方案**:找出总分最低的(最优)和最高的(最劣)方案。
```matlab
[~, best_idx] = min(total_score, 'first');
worst_idx = find(total_score == max(total_score));
```
6. **输出结果**:最后显示排名顺序及相应的评分。
```matlab
best_solution = data(best_idx,:);
worst_solution = data(worst_idx,:);
disp(['Best solution: ', char(best_solution)]);
disp(['Worst solution: ', char(worst_solution)]);
```
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