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创建5-20-2的BP神经网络，即输入层为5个神经元，隐藏层为20个神经元，输出为2个神经网络的BP神经网络。
x=rand(5,1000);%输入为5维度共1000个数据 y(1,:)=sin(3*sum(x,1));%输出的第一维数据 y(2,:)=cos(5
*sum(x,1));%输出的第二维数据 %% 训练网络 P=x;%输入数据 T=y;%输出数据 net = newff(P,T,20);%建立BP神经网络
含20个隐藏神经元 net.trainParam.epochs= 1000;%迭代次数 net.trainParam.goal = 1e-20;%学习目标
net.trainParam.lr= 0.01;%学习率 net = train(net,P,T); %% 测试网络 A = sim(net,P); %%
画出图像 figure plot(A(1,:),'r*'); hold on plot(T(1,:),'bo'); legend('预测值','真实值')
xlabel('n') ylabel('y1') figure plot(A(2,:),'r*'); hold on plot(T(2,:),'bo');
legend('预测值','真实值') xlabel('n') ylabel('y2') figure plot(A(1,:),A(2,:),'r*');
hold on plot(T(1,:),T(2,:),'bo'); legend('预测值','真实值') xlabel('y1') ylabel('y2')
figure plot(abs(A(1,:)-T(1,:)),'r-o'); hold on plot(abs(A(2,:)-T(2,:)),'b-+');
xlabel('n') ylabel('MAE') legend('y1','y2')
结果：
目标1真实值和预测值：
目标2真实值和预测值：
目标1与目标2的真实值和预测值：
预测值和真实值的绝对误差：
