回归预测 | Matlab基于CPO-GPR基于冠豪猪算法优化高斯过程回归的多输入单输出回归预测
目录
预测效果
基本介绍
Matlab基于CPO-GPR基于冠豪猪算法优化高斯过程回归的数据回归预测（完整源码和数据)
1.Matlab基于CPO-GPR基于冠豪猪算法优化高斯过程回归的输入多个特征，输出单个变量，多输入单输出回归预测；
3.多指标评价，评价指标包括：R2、MAE、MSE、RMSE等，代码质量极高；
4.粒子群算法优化参数为：优化核函数超参数 sigma，标准差，初始噪声标准差；
5.excel数据，方便替换，运行环境2018及以上。
程序设计
- 完整程序和数据获取方式(资源处下载):Matlab基于CPO-GPR基于冠豪猪算法优化高斯过程回归的数据回归预测
clike
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 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
% restoredefaultpath
%% 导入数据
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
f_ =size(P_train, 1); %输入特征维度
M = size(P_train, 2);
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);
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 转置以适应模型
p_train = p_train'; p_test = p_test';
t_train = t_train'; t_test = t_test';
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 超参数设置
Best_pos = [0.6, 0.7, 30]; % 优化下界
%% 仿真测试
t_sim1 = predict(net, p_train);
t_sim2 = predict(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);
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 数据转置
T_sim1=T_sim1';
T_sim2 =T_sim2';
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 均方根误差
error1 = sqrt(sum((T_sim1 - T_train).^2) ./ M);
error2 = sqrt(sum((T_sim2 - T_test ).^2) ./ N);
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%
%决定系数
R1 = 1 - norm(T_train - T_sim1)^2 / norm(T_train - mean(T_train))^2;
R2 = 1 - norm(T_test - T_sim2)^2 / norm(T_test - mean(T_test ))^2;
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%
%均方误差 MSE
mse1 = sum((T_sim1 - T_train).^2)./M;
mse2 = sum((T_sim2 - T_test).^2)./N;
%%
%RPD 剩余预测残差
SE1=std(T_sim1-T_train);
RPD1=std(T_train)/SE1;
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SE=std(T_sim2-T_test);
RPD2=std(T_test)/SE;
%% 平均绝对误差MAE
MAE1 = mean(abs(T_train - T_sim1));
MAE2 = mean(abs(T_test - T_sim2));
%% 平均绝对百分比误差MAPE
MAPE1 = mean(abs((T_train - T_sim1)./T_train));
MAPE2 = mean(abs((T_test - T_sim2)./T_test));
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 测试集误差图
figure
ERROR3=T_test-T_sim2;
plot(T_test-T_sim2,'b-*','LineWidth',1.5)
xlabel('测试集样本编号')
ylabel('预测误差')
title('测试集预测误差')
grid on;
legend('GPR预测输出误差')
%% 打印出评价指标
disp(['-----------------------误差计算--------------------------'])
disp(['评价结果如下所示：'])
disp(['平均绝对误差MAE为：',num2str(MAE2)])
disp(['均方误差MSE为： ',num2str(mse2)])
disp(['均方根误差RMSEP为： ',num2str(error2)])
disp(['决定系数R^2为： ',num2str(R2)])
disp(['剩余预测残差RPD为： ',num2str(RPD2)])
disp(['平均绝对百分比误差MAPE为： ',num2str(MAPE2)])参考资料
[1]https://blog.csdn.net/kjm13182345320/article/details/124443069?spm=1001.2014.3001.5501
[2]https://blog.csdn.net/kjm13182345320/article/details/124443735?spm=1001.2014.3001.5501