MAT之NN：實現BP神經網絡的回歸擬合，基于近紅外光譜的汽油辛烷值含量預測結果對比

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目錄

輸出結果

實現代碼

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輸出結果

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實現代碼

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：實現BP神經網絡的回歸擬合，基于近紅外光譜的汽油辛烷值含量

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