JCR一区级 | Matlab实现BO-Transformer-LSTM多变量回归预测
目录
效果一览
基本介绍
1.Matlab实现BO-Transformer-LSTM多变量回归预测，贝叶斯优化Transformer结合LSTM长短期记忆神经网络多变量回归预测，BO-Transformer-LSTM/Bayes-Transformer-LSTM（程序可以作为JCR一区级论文代码支撑，目前尚未发表）；
2.贝叶斯优化参数为：学习率，LSTM隐含层节点，正则化参数，运行环境为Matlab2023b及以上；
3.data为数据集，输入多个特征，输出单个变量，多变量回归预测，main.m为主程序，运行即可,所有文件放在一个文件夹；
4.命令窗口输出R2、MSE、RMSE、MAE、MAPE、MBE等多指标评价；
程序设计
- 完整程序和数据下载私信博主回复Matlab实现BO-Transformer-LSTM多变量回归预测。
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
%% 划分训练集和测试集
P_train = res(1: num_train_s, 1: f_)';
T_train = res(1: num_train_s, f_ + 1: end)';
M = size(P_train, 2);
P_test = res(num_train_s + 1: end, 1: f_)';
T_test = res(num_train_s + 1: end, f_ + 1: end)';
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);
%% 数据平铺
P_train = double(reshape(P_train, f_, 1, 1, M));
P_test = double(reshape(P_test , f_, 1, 1, N));
t_train = t_train';
t_test = t_test' ;
%% 数据格式转换
for i = 1 : M
p_train{i, 1} = P_train(:, :, 1, i);
end
for i = 1 : N
p_test{i, 1} = P_test( :, :, 1, i);
end
%% 创建待优化函数
ObjFcn = @BOFunction;
%% 贝叶斯优化参数范围
optimVars = [参考资料
[1] https://blog.csdn.net/kjm13182345320/article/details/128163536?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/128151206?spm=1001.2014.3001.5502