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基于云模型的模糊综合风险评估Matlab代码

news 2025/8/4 5:45:44

一、核心代码结构

%% 主程序框架
clc; clear; close all;%% 参数设置
N = 5000; % 云滴数
levels = [1,3,5,7,9]; % 风险等级中心值
En = 0.3333; He = 0.03; % 标准云参数%% 数据输入
expert_scores = xlsread('expert_scores.xlsx'); % 专家评分矩阵
weights = [0.2,0.3,0.5]; % 指标权重向量%% 云模型构建
[standard_cloud] = build_standard_cloud(levels, En, He); % 标准云生成
indicator_clouds = calculate_indicator_clouds(expert_scores, N); % 指标云计算
composite_cloud = combine_clouds(indicator_clouds, weights); % 综合云生成%% 可视化分析
figure;
plot_standard_cloud(standard_cloud); % 绘制标准云图
hold on;
plot_cloud(composite_cloud, 'r', 2); % 绘制综合云图
legend('标准云', '综合云');
title('风险等级云图对比');%% 相似度计算
similarity = calculate_similarity(composite_cloud, standard_cloud);
disp(['综合风险等级相似度：', num2str(similarity)]);%% 辅助函数
function cloud = build_standard_cloud(levels, En, He)% 标准云生成函数cloud = struct();for i = 1:length(levels)[X,Y] = cloud_generator(levels(i), En, He, N);cloud(i).X = X; cloud(i).Y = Y;cloud(i).Ex = levels(i); cloud(i).En = En; cloud(i).He = He;end
endfunction [X,Y] = cloud_generator(Ex, En, He, N)% 正向云发生器X = zeros(1,N);Y = zeros(1,N);En_prime = normrnd(En, He, 1,N);for i = 1:NX(i) = normrnd(Ex, En_prime(i));Y(i) = exp(-(X(i)-Ex).^2/(2*En_prime(i)^2));end
end

二、关键算法实现

1. 多级指标云模型计算

function indicator_clouds = calculate_indicator_clouds(data, N)% 多级指标云模型计算[m,n] = size(data);indicator_clouds = cell(m,1);for i = 1:m% 逆向云发生器计算参数Ex = mean(data(:,i));En = std(data(:,i)) * 0.618; % 黄金分割系数He = sqrt(var(data(:,i)) - En^2) * 0.1;% 生成云滴[X,Y] = cloud_generator(Ex, En, He, N);indicator_clouds{i}.X = X;indicator_clouds{i}.Y = Y;indicator_clouds{i}.Ex = Ex;indicator_clouds{i}.En = En;indicator_clouds{i}.He = He;end
end

2. 云模型加权综合

function composite = combine_clouds(indicator_clouds, weights)% 云模型加权综合算法num_indicators = length(indicator_clouds);composite.Ex = 0;composite.En = 0;composite.He = 0;for i = 1:num_indicatorsw = weights(i);composite.Ex = composite.Ex + w * indicator_clouds{i}.Ex;composite.En = composite.En + w * indicator_clouds{i}.En;composite.He = composite.He + w * indicator_clouds{i}.He;end% 误差传播修正composite.En = composite.En * sqrt(sum(w.^2));composite.He = composite.He * sqrt(sum(w.^2));
end

三、可视化模块

1. 标准云图绘制

function plot_standard_cloud(standard_cloud)% 绘制标准风险等级云图colors = hsv(5);for i = 1:5plot(standard_cloud(i).X, standard_cloud(i).Y, ...'Color', colors(i,:), 'Marker', '.', 'MarkerSize', 10);endhold on;xlabel('风险值');ylabel('隶属度');xlim([0 10]);ylim([0 1]);grid on;
end

2. 综合云图生成

function plot_cloud(cloud, color, lineWidth)% 绘制综合云图plot(cloud.X, cloud.Y, 'Color', color, ...'Marker', '.', 'MarkerSize', 8, 'LineWidth', lineWidth);
end

四、相似度计算算法

function similarity = calculate_similarity(cloud1, cloud2)% 基于蒙特卡洛方法的云相似度计算N = length(cloud1.X);overlap = 0;for i = 1:Nd = sqrt((cloud1.X(i)-cloud2.X(i))^2 + ...(cloud1.Y(i)-cloud2.Y(i))^2);if d < 0.1overlap = overlap + 1;endendsimilarity = overlap / N;
end