MAT之NN：实现BP神经网络的回归拟合，基于近红外光谱的汽油辛烷值含量预测结果对比load spectra_data.

MAT之NN：实现BP神经网络的回归拟合，基于近红外光谱的汽油辛烷值含量预测结果对比

输出结果

实现代码

load spectra_data.mat
plot(NIR')  
title('Near infrared spectrum curve—Jason niu')
 
temp = randperm(size(NIR,1));
P_train = NIR(temp(1:50),:)';
T_train = octane(temp(1:50),:)';
P_test = NIR(temp(51:end),:)';
T_test = octane(temp(51:end),:)';
N = size(P_test,2); 
 
[p_train, ps_input] = mapminmax(P_train,0,1); 
p_test = mapminmax('apply',P_test,ps_input); 
 
[t_train, ps_output] = mapminmax(T_train,0,1);  
 
net = newff(p_train,t_train,9);
 
net.trainParam.epochs = 1000;
net.trainParam.goal = 1e-3;  
net.trainParam.lr = 0.01;   
 
net = train(net,p_train,t_train); 
 
t_sim = sim(net,p_test);
 
T_sim = mapminmax('reverse',t_sim,ps_output);
 
error = abs(T_sim - T_test)./T_test;
 
R2 = (N * sum(T_sim .* T_test) - sum(T_sim) * sum(T_test))^2 / ((N * sum((T_sim).^2) - (sum(T_sim))^2) * (N * sum((T_test).^2) - (sum(T_test))^2));
 
result = [T_test' T_sim' error']
 
figure
plot(1:N,T_test,'b:*',1:N,T_sim,'r-o')
legend('Real value','predicted value')
xlabel('Prediction sample')
ylabel('Octane numbe')
string = {'Comparison of the prediction results of the octane number in the test set—Jason niu';['R^2=' num2str(R2)]};
title(string)