the code
clear
close all
clc
input=[1 13.3 7.7 10.5 69.00000001 20.07
1.5 13.05 7.7 10.4 70 20.43
2 12.8 7.7 10.3 71 17.92
2.5 12.75 7.8 10.3 72 17.48
3 12.7 7.9 10.3 73 17
3.5 12.7 8.100000001 10.4 74 16.81
4 12.7 8.300000001 10.5 75.00000001 16.43
4.5 12.6 8.4 10.5 76 16.19
5 12.5 8.5 10.5 77 16.68
5.5 12.4 8.149999999 10.3 75.5 17.2
6 12.3 7.8 10.1 74 17.68
6.5 12.25 7.9 10.1 74.5 22.87
7 12.2 7.999999998 10.1 74.99999999 25.38
7.5 12.4 7.6 10.05 72.5 26.45
8 12.6 7.2 10 70 31.75
8.5 13.15 7.850000002 10.55 70.5 35.56
9 13.7 8.5 11.1 71 27.24
9.5 14.6 8.800000001 11.65 68.49999999 26.23
10 15.5 9.100000001 12.2 65.99999999 25.26
10.5 16.05 9.2 12.5 64 24.76
11 16.6 9.3 12.8 62 23.39
11.5 16.65 9.900000002 13.1 64.50000001 23.06
12 16.7 10.5 13.4 67 22.57
12.5 17.1 9.899999998 13.3 62.99999999 22.62
13 17.5 9.299999999 13.2 58.99999999 22.33
13.5 17.6 9.05 13.15 57.5 22.28
14 17.7 8.8 13.1 56 22.05
14.5 17.75 8.549999999 13 55 21.19
15 17.8 8.3 12.9 54 19.87
15.5 17.2 9.050000002 12.95 59.00000001 19.49]; %载入输入数据
output=[8974.73
8819.75
8593.21
8165.02
7792.29
7451
7262.58
7192.3
7252.37
7544.07
8020.28
8986.43
9922.95
10337.4
10769.53
10808.89
10644.19
10541.34
10437.73
10324.85
10231.09
10045.83
9959.69
9865.52
9779.02
9705.81
9635.86
9636.27
9610.29
9562.34
];
input_train = input(1:27,:)';
output_train =output(1:27,:)';
input_test = input(28:30,:)';
output_test =output(28:30,:)';
inputnum=2;
hiddennum=5;
outputnum=1;
[inputn,inputps]=mapminmax(input_train);%% 归一化
[outputn,outputps]=mapminmax(output_train);
net=newff(inputn,outputn,hiddennum,{
'tansig','purelin'},'trainlm');% purelin传递函数，梯度下降法
W1= net. iw{
1, 1};
B1 = net.b{
1};
W2 = net.lw{
2,1};
B2 = net. b{
2};
net.trainParam.epochs=1000; % 训练次数
net.trainParam.lr=0.01; % 学习速率
net.trainParam.goal=0.00001; % 训练目标最小误差
net.dividefcn='';%关闭过拟合解烨功能
net=train(net,inputn,outputn);
inputn_test=mapminmax('apply',input_test,inputps);
an=sim(net,inputn_test); %进行仿真
an0=sim(net,inputn);%对训练集仿真
train_simu=mapminmax('reverse',an0,outputps);
figure('units','normalized','position',[0.119 0.2 0.38 0.5])
plot(output_train,'bo-','markerfacecolor','b')
hold on
plot(train_simu,'rs-','markerfacecolor','r')
grid on
%hold on
%plot(error,'square','MarkerFaceColor','b')
legend('期望值','预测值','误差')
xlabel('数据组数')
ylabel('样本值')
title('极限学习机ELM测试集的预测值与实际值对比图')
test_simu=mapminmax('reverse',an,outputps);
error=test_simu-output_test;
figure('units','normalized','position',[0.5 0.2 0.38 0.5])
plot(output_test,'bo-','markerfacecolor','b')
hold on
plot(test_simu,'rs-','markerfacecolor','r')
grid on
%hold on
%plot(error,'square','MarkerFaceColor','b')
legend('期望值','预测值')
xlabel('数据组数')
ylabel('样本值')
title('极限学习机ELM测试集的预测值与实际值对比图')
[c,l]=size(output_test);
MAE1=sum(abs(error))/l;
MSE1=error*error'/l;
RMSE1=MSE1^(1/2);
MAPE1=sum(abs(error/output_test)/l);
R=corrcoef(output_test,test_simu);
R2=R(1,2)^2;%0.75以上就代表非常好了
disp(['-----------------------误差计算--------------------------'])
disp(['隐含层节点数为',num2str(hiddennum),'时的误差结果如下：'])
disp(['平均绝对误差MAE为：',num2str(MAE1)])
disp(['均方误差MSE为： ',num2str(MSE1)])
disp(['均方根误差RMSE为： ',num2str(RMSE1)])
disp(['相对平均误差MAPE为： ',num2str(MAPE1)])
disp(['绝对系数R^2为： ',num2str(R2)])
%导入用来预测的数据
A1=[16
16.6
9.799999996
13
63.99999998
19.66
];
%预测输出
Predict0=sim(net,A1)%根据输入数据得到预测数据
Predict=mapminmax('reverse',Predict0,outputps);
disp(['预测下一天的数据为： ',num2str(Predict)])
result
If you need code and data, please send an email in the comment area, usually a reply will be made within one day, please like + pay attention, thank you! !