MAT之SVM/BP：SVR(better)和BP兩種方法比較且實現建筑物鋼筋混凝土抗壓強度預測

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代碼設計

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代碼設計

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:mfor j = 1:ncmd = ['-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) < errorerror = cg(i,j);bestc = 2^c(i,j);bestg = 2^g(i,j);endif 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);endend endcmd = [' -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('真實值','預測值') xlabel('樣本編號') ylabel('耐壓強度') string_1 = {'訓練集預測結果對比(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('真實值','預測值') xlabel('樣本編號') ylabel('耐壓強度') string_2 = {'SVM之SVR測試集預測結果對比(SVM之SVR)—Jason niu';['mse = ' num2str(error_2(2)) ' R^2 = ' num2str(error_2(3))]}; title(string_2)%BP神經網絡 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('真實值','預測值') xlabel('樣本編號') ylabel('耐壓強度') string_3 = {'測試集預測結果對比(BP神經網絡)—Jason niu';['mse = ' num2str(E) ' R^2 = ' num2str(R2)]}; title(string_3)

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