%% 第29章 支持向量机的回归拟合——混凝土抗压强度预测
% <html>
% <table border="0" width="600px" id="table1"> <tr> <td><b><font size="2">该案例作者申明：</font></b></td> </tr> <tr><td><span class="comment"><font size="2">1：本人长期驻扎在此<a target="_blank" href="http://www.matlabsky.com/forum-78-1.html"><font color="#0000FF">板块</font></a>里，对该案例提问，做到有问必答。</font></span></td></tr><tr> <td><span class="comment"><font size="2">2</font><font size="2">：此案例有配套的教学视频，视频下载请点击<a href="http://www.matlabsky.com/forum-91-1.html">http://www.matlabsky.com/forum-91-1.html</a></font><font size="2">。 </font></span></td> </tr> <tr> <td><span class="comment"><font size="2"> 3：此案例为原创案例，转载请注明出处（《MATLAB智能算法30个案例分析》）。</font></span></td> </tr> <tr> <td><span class="comment"><font size="2"> 4：若此案例碰巧与您的研究有关联，我们欢迎您提意见，要求等，我们考虑后可以加在案例里。</font></span></td> </tr> <tr> <td><span class="comment"><font size="2"> 5：以下内容为初稿，与实际发行的书籍内容略有出入，请以书籍中的内容为准。</font></span></td> </tr> </table>
% </html>
%% 清空环境变量
clear all
clc
%% 导入数据
load concrete_data.mat
% 随机产生训练集和测试集
n = randperm(size(attributes,2));
% 训练集——80个样本
p_train = attributes(:,n(1:80))';
t_train = strength(:,n(1:80))';
% 测试集——23个样本
p_test = attributes(:,n(81:end))';
t_test = strength(:,n(81:end))';
%% 数据归一化
% 训练集
[pn_train,inputps] = mapminmax(p_train');
pn_train = pn_train';
pn_test = mapminmax('apply',p_test',inputps);
pn_test = pn_test';
tn_train=t_train;
tn_test=t_test;
%% SVM模型创建/训练
% 寻找最佳c参数/g参数
[c,g] = meshgrid(-10:0.5:10,-10:0.5:10);
[m,n] = size(c);
cg = zeros(m,n);
eps = 10^(-4);
v = 5;
bestc = 0;
bestg = 0;
error = Inf;
for i = 1:m
for j = 1:n
cmd = ['-v ',num2str(v),' -t 2',' -c ',num2str(2^c(i,j)),' -g ',num2str(2^g(i,j) ),' -s 3 -p 0.1'];
cg(i,j) = svmtrain(tn_train,pn_train,cmd);
if cg(i,j) < error
error = cg(i,j);
bestc = 2^c(i,j);
bestg = 2^g(i,j);
end
if abs(cg(i,j) - error) <= eps && bestc > 2^c(i,j)
error = cg(i,j);
bestc = 2^c(i,j);
bestg = 2^g(i,j);
end
end
end
% 创建/训练SVM
bestc=1024;
bestg=0.0442;
cmd1 = [' -t 2',' -c ',num2str(bestc),' -g ',num2str(bestg),' -s 3 -p 0.01'];
model1 = svmtrain(tn_train,pn_train,cmd1);
cmd2 = ['-t 2 -c 724.0773 -g 0.0625 -s 3 -p 0.01'];
model2 = svmtrain(tn_train,pn_train,cmd2);
%% SVM仿真预测
[predict_1] = svmpredict(tn_train,pn_train,model1);
[predict_2] = svmpredict(tn_test,pn_test,model1);
result_1 = [t_train predict_1];
result_2 = [t_test predict_2];
[predict_3] = svmpredict(tn_train,pn_train,model2);
[predict_4] = svmpredict(tn_test,pn_test,model2);
result_3 = [t_train predict_3];
result_4 = [t_test predict_4];
%% 绘图
figure(1)
plot(1:length(t_train),t_train,'r-*',1:length(t_train),predict_1,'b:o')
grid on
legend('真实值','预测值')
xlabel('样本编号')
ylabel('耐压强度')
figure(2)
plot(1:length(t_test),t_test,'r-*',1:length(t_test),predict_2,'b:o')
grid on
legend('真实值','预测值')
xlabel('样本编号')
ylabel('耐压强度')
% string_2 = {'测试集预测结果对比';
% ['mse = ' num2str(error_2(2)) ' R^2 = ' num2str(error_2(3))]};
% title(string_2)
%% BP 神经网络
% 数据转置
pn_train = pn_train';
tn_train = tn_train';
pn_test = pn_test';
tn_test = tn_test';
% 创建BP神经网络
net = newff(pn_train,tn_train,10);
% 设置训练参数
net.trainParam.epcohs = 1000;
net.trainParam.goal = 1e-3;
net.trainParam.show = 10;
net.trainParam.lr = 0.1;
% 训练网络
net = train(net,pn_train,tn_train);
% 仿真测试
tn_sim = sim(net,pn_test);
% 均方误差
E = mse(tn_sim - tn_test);
% 决定系数
N = size(t_test,1);
R2=(N*sum(tn_sim.*tn_test)-sum(tn_sim)*sum(tn_test))^2/((N*sum((tn_sim).^2)-(sum(tn_sim))^2)*(N*sum((tn_test).^2)-(sum(tn_test))^2));
% 反归一化
t_sim = mapminmax('reverse',tn_sim,outputps);
% 绘图
figure(3)
plot(1:length(t_test),t_test,'r-*',1:length(t_test),t_sim,'b:o')
grid on
legend('真实值','预测值')
xlabel('样本编号')
ylabel('耐压强度')
string_3 = {'测试集预测结果对比(BP神经网络)';
['mse = ' num2str(E) ' R^2 = ' num2str(R2)]};
title(string_3)
%%
% <html>
% <table width="656" align="left" > <tr><td align="center"><p align="left"><font size="2">相关论坛：</font></p><p align="left"><font size="2">Matlab技术论坛：<a href="http://www.matlabsky.com">www.matlabsky.com</a></font></p><p align="left"><font size="2">M</font><font size="2">atlab函数百科：<a href="http://www.mfun.la">www.mfun.la</a></font></p></td> </tr></table>
% </html>
评论0