matlab实现BP神经网络的程序源码

clear; %清空环境变量 clc; % 训练集/测试集的产生，这里采用xlsread（''）； paifangshuju=xlsread('E:\paifangshuju2.xlsx'); NIR=paifangshuju(:,1:2); octane=paifangshuju(:,3:6); % % 随机产生训练集和测试集 temp = randperm(size(NIR,1)); %训练集--80个样本 P_train = NIR(temp(1:80),:)'; T_train = octane(temp(1:80),:)'; %测试集--10个样本 P_test = NIR(temp(81:end),:)'; T_test = octane(temp(81:end),:)'; N = size(P_test,2); %验证、训练数据归一化 [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); % % BP神经网络的创建、训练及仿真测试 %创建网络 net = newff(p_train,t_train,[4,8]);%2为隐含层的层数 %设置训练参数 net.trainParam.epochs=1000; net.trainParam.goal=1e-3; net.trainParam.lr=0.01; %训练网络 net=train(net,p_train,t_train); %仿真测试 t_sim=sim(net,p_test); %数据反归一化 T_sim=mapminmax('reverse',t_sim,ps_output); %%性能评价 %相对误差 error=abs(T_sim-T_test)./T_test; %均方根误差 for i=1:4,j=1:N; A(i,j)=T_test(i,j)-T_sim(i,j); end MSE_HC=sqrt(sum(A(1,:).^2)); MSE_CO=sqrt(sum(A(2,:).^2)); MSE_NO=sqrt(sum(A(3,:).^2)); MSE_O2=sqrt(sum(A(4,:).^2)); MSE=[MSE_HC;MSE_CO;MSE_NO;MSE_O2]; %决定系数R^2 P_test T_train P_train T_test T_sim 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)); %对比结果 result=[T_test' T_sim' error']; %绘图 subplot(2,2,1); a = 1:1:10; %横坐标 b = T_test(1,:); %纵坐标 %第二种，画平滑曲线的方法 values = spcrv([[a(1) a a(end)];[b(1) b b(end)]],3); plot(values(1,:),values(2,:), 'k'); hold on; a = 1:1:10; %横坐标 plot(a,T_sim(1,:),'sk','MarkerFaceColor','k'); xlabel('\bf发动机转速/rpm'); ylabel('\bfHC/10^{-6}'); hold on; xL=xlim;yL=ylim; plot(xL,[yL(2),yL(2)],'k',[xL(2),xL(2)],[yL(1),yL(2)],'k'); box off; axis([xL yL]); legend('\bf真实值','\bf预测值'); title('\bf测试HC含量预测结果对比图'); set(gca,'XTickLabel',{'3008','3503','4010','5502','6013','6511','7010','7509','8504','9002'}); subplot(2,2,2); a = 1:1:10; %横坐标 c = T_test(2,:); %纵坐标 %第二种，画平滑曲线的方法 values = spcrv([[a(1) a a(end)];[c(1) c c(end)]],3); plot(values(1,:),values(2,:), 'k'); hold on; a = 1:1:10; %横坐标 plot(a,T_sim(2,:),'sk','MarkerFaceColor','k'); legend('\bf真实值','\bf预测值'); title('\bf测试CO含量预测结果对比图'); xlabel('\bf发动机转速/rpm'); ylabel('\bfCO/%'); hold on; xL=xlim;yL=ylim; plot(xL,[yL(2),yL(2)],'k',[xL(2),xL(2)],[yL(1),yL(2)],'k'); box off; axis([xL yL]); set(gca,'XTickLabel',{'3008','3503','4010','5502','6013','6511','7010','7509','8504','9002'}); subplot(2,2,3); a = 1:1:10; %横坐标 d = T_test(3,:); %纵坐标 %第二种，画平滑曲线的方法 values = spcrv([[a(1) a a(end)];[d(1) d d(end)]],3); plot(values(1,:),values(2,:), 'k'); hold on; a = 1:1:10; %横坐标 plot(a,T_sim(3,:),'sk','MarkerFaceColor','k'); xlabel('\bf发动机转速/rpm'); ylabel('\bfO_{2}/%'); hold on; xL=xlim;yL=ylim; plot(xL,[yL(2),yL(2)],'k',[xL(2),xL(2)],[yL(1),yL(2)],'k'); box off; axis([xL yL]); legend('\bf真实值','\bf预测值'); title('\bf测试O_{2}含量预测结果对比图'); set(gca,'XTickLabel',{'3008','3503','4010','5502','6013','6511','7010','7509','8504','9002'}); subplot(2,2,4); a = 1:1:10; %横坐标 e = T_test(4,:); %纵坐标 %第二种，画平滑曲线的方法 values = spcrv([[a(1) a a(end)];[e(1) e e(end)]],3); plot(values(1,:),values(2,:), 'k'); hold on; a = 1:1:10; %横坐标 plot(a,T_sim(4,:),'sk','MarkerFaceColor','k'); xlabel('\bf发动机转速/rpm'); ylabel('\bfNO_{X}/10^{-6}'); hold on; xL=xlim;yL=ylim; plot(xL,[yL(2),yL(2)],'k',[xL(2),xL(2)],[yL(1),yL(2)],'k'); box off; axis([xL yL]); legend('\bf真实值','\bf预测值'); title('\bf测试NO_{X}含量预测结果对比图'); set(gca,'XTickLabel',{'3008','3503','4010','5502','6013','6511','7010','7509','8504','9002'}); saveas(1,'filename','bmp');