多输入多输出 | MATLAB实现GWO-Elman灰狼优化循环神经网络多输入多输出预测
目录
- 多输入多输出 | MATLAB实现GWO-Elman灰狼优化循环神经网络多输入多输出预测
- 预测效果
- 基本介绍
- 程序设计
- 往期精彩
- 参考资料
预测效果
基本介绍
Matlab实现GWO-Elman灰狼优化循环神经网络多输入多输出预测
1.data为数据集,10个输入特征,3个输出变量。
2.main.m为主程序文件。
程序设计
- 完整程序和数据下载方式私信博主回复:Matlab实现GWO-Elman灰狼优化循环神经网络多输入多输出预测。
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 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('data.xlsx');
%-------------------------------------------------------------
%% 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);
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 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);
往期精彩
MATLAB实现RBF径向基神经网络多输入多输出预测
MATLAB实现BP神经网络多输入多输出预测
MATLAB实现DNN神经网络多输入多输出预测
参考资料
[1] https://blog.csdn.net/kjm13182345320/article/details/126864256
[2] https://blog.csdn.net/kjm13182345320/article/details/126019698
[3] https://blog.csdn.net/kjm13182345320/article/details/125237445