输出结果
代码设计
load concrete_data.mat
n = randperm(size(attributes,2));
p_train = attributes(:,n(1:80))';
t_train = strength(:,n(1:80))';
p_test = attributes(:,n(81:end))';
t_test = strength(:,n(81:end))';
[pn_train,inputps] = mapminmax(p_train');
pn_train = pn_train';
pn_test = mapminmax('apply',p_test',inputps);
pn_test = pn_test';
[tn_train,outputps] = mapminmax(t_train');
tn_train = tn_train';
tn_test = mapminmax('apply',t_test',outputps);
tn_test = tn_test';
[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
cmd = [' -t 2',' -c ',num2str(bestc),' -g ',num2str(bestg),' -s 3 -p 0.01'];
model = svmtrain(tn_train,pn_train,cmd);
[Predict_1,error_1] = svmpredict(tn_train,pn_train,model);
[Predict_2,error_2] = svmpredict(tn_test,pn_test,model);
predict_1 = mapminmax('reverse',Predict_1,outputps);
predict_2 = mapminmax('reverse',Predict_2,outputps);
result_1 = [t_train predict_1];
result_2 = [t_test predict_2];
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')
string_1 = {'训练集预测结果对比(SVM之SVR)—Jason niu';
['mse = ' num2str(error_1(2)) ' R^2 = ' num2str(error_1(3))]};
title(string_1)
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 = {'SVM之SVR测试集预测结果对比(SVM之SVR)—Jason niu';
['mse = ' num2str(error_2(2)) ' R^2 = ' num2str(error_2(3))]};
title(string_2)
%BP神经网络
pn_train = pn_train';
tn_train = tn_train';
pn_test = pn_test';
tn_test = tn_test';
net = newff(pn_train,tn_train,10);
net.trainParam.epochs = 1000;
net.trainParam.goal = 1e-3;
net.trainParam.show = 10;
net.trainParam.lr = 0.1;
net = train(net,pn_train,tn_train);
tn_sim = sim(net,pn_test);
E = mse(tn_sim - tn_test);
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));
t_sim = mapminmax('reverse',tn_sim,outputps);
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)—Jason niu';
['mse = ' num2str(E) ' R^2 = ' num2str(R2)]};
title(string_3)
