%% 清空环境变量
warning off % 关闭报警信息
close all % 关闭开启的图窗
clear % 清空变量
clc % 清空命令行
%% 导入数据
res = xlsread('数据集.xlsx');
%% 划分训练集和测试集
temp = randperm(600);
P_train = res(temp(1: 500), 1 : 28)';
T_train = res(temp(1: 500), 29: 31)';
M = size(P_train, 2);
P_test = res(temp(501: end), 1 : 28)';
T_test = res(temp(501: end), 29: 31)';
N = size(P_test, 2);
%% 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);
t_test = mapminmax('apply', T_test, ps_output);
%% 创建网络
net = newff(p_train, t_train, 10);
%% 设置训练参数
net.trainParam.epochs = 1000; % 迭代次数
net.trainParam.goal = 1e-6; % 误差阈值
net.trainParam.lr = 0.01; % 学习率
net.trainFcn = 'trainlm';
%% 训练网络
net = train(net, p_train, t_train);
%% 仿真测试
t_sim1 = sim(net, p_train);
t_sim2 = sim(net, p_test );
%% 数据反归一化
T_sim1 = mapminmax('reverse', t_sim1, ps_output);
T_sim2 = mapminmax('reverse', t_sim2, ps_output);
for i = 1: 3
%% 均方根误差
error1(i, :) = sqrt(sum((T_sim1(i, :) - T_train(i, :)).^2) ./ M);
error2(i, :) = sqrt(sum((T_sim2(i, :) - T_test (i, :)).^2) ./ N);
%% 绘图
figure
subplot(2, 1, 1)
plot(1: M, T_train(i, :), 'r-*', 1: M, T_sim1(i, :), 'b-o', 'LineWidth', 1)
legend('真实值','预测值')
xlabel('预测样本')
ylabel('预测结果')
string = {'训练集预测结果对比'; ['RMSE=' num2str(error1(i, :))]};
title(string)
xlim([1, M])
grid
subplot(2, 1, 2)
plot(1: N, T_test(i, :), 'r-*', 1: N, T_sim2(i, :), 'b-o', 'LineWidth', 1)
legend('真实值','预测值')
xlabel('预测样本')
ylabel('预测结果')
string = {'测试集预测结果对比';['RMSE=' num2str(error2(i, :))]};
title(string)
xlim([1, N])
grid
%% 分割线
disp('**************************')
disp(['下列是输出', num2str(i)])
disp('**************************')
%% 相关指标计算
% 决定系数 R2
R1(i, :) = 1 - norm(T_train(i, :) - T_sim1(i, :))^2 / norm(T_train(i, :) - mean(T_train(i, :)))^2;
R2(i, :) = 1 - norm(T_test (i, :) - T_sim2(i, :))^2 / norm(T_test (i, :) - mean(T_test (i, :)))^2;
disp(['训练集数据的R2为：', num2str(R1(i, :))])
disp(['测试集数据的R2为：', num2str(R2(i, :))])
% 平均绝对误差 MAE
mae1(i, :) = sum(abs(T_sim1(i, :) - T_train(i, :))) ./ M ;
mae2(i, :) = sum(abs(T_sim2(i, :) - T_test (i, :))) ./ N ;
disp(['训练集数据的MAE为：', num2str(mae1(i, :))])
disp(['测试集数据的MAE为：', num2str(mae2(i, :))])
% 平均相对误差 MBE
mbe1(i, :) = sum(T_sim1(i, :) - T_train(i, :)) ./ M ;
mbe2(i, :) = sum(T_sim2(i, :) - T_test (i, :)) ./ N ;
disp(['训练集数据的MBE为：', num2str(mbe1(i, :))])
disp(['测试集数据的MBE为：', num2str(mbe2(i, :))])
end
