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

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)

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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

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