Regression prediction | MATLAB implements CHOA-BiLSTM chimpanzee optimization algorithm to optimize bidirectional long short-term memory network regression prediction (multiple indicators, multiple graphs)

Regression prediction | MATLAB implements CHOA-BiLSTM chimpanzee optimization algorithm to optimize bidirectional long short-term memory network regression prediction (multiple indicators, multiple graphs)

Effect list

basic introduction

CHOA-BiLSTM Chimpanzee Optimization Algorithm Optimizes Bidirectional Long Short-term Memory Network Regression Prediction Matlab Program
1. The multi-variable input single output effect is shown in the figure. Few people use the algorithm~ 4. Comes with sample data and can be run directly. 3. It can be used by directly replacing the Excel data, with clear annotations, suitable for novices. 3. Comes with case data and directly run main to generate graphs with one click~
2. Directly replace the Excel data and use it. Suitable for novices~

programming

• Complete source code and data acquisition method: private message replyMATLAB implements CHOA-BiLSTM chimpanzee optimization algorithm to optimize bidirectional long short-term memory network regression prediction (multiple indicators, multiple graphs) .

%%  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');

%%  划分训练集和测试集
temp = randperm(103);

P_train = res(temp(1: 80), 1: 7)';
T_train = res(temp(1: 80), 8)';
M = size(P_train, 2);

P_test = res(temp(81: end), 1: 7)';
T_test = res(temp(81: end), 8)';
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);



%%  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);

%%  均方根误差
error1 = sqrt(sum((T_sim1 - T_train).^2) ./ M);
error2 = sqrt(sum((T_sim2 - T_test ).^2) ./ N);



%%  相关指标计算
% 决定系数 R2
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;

disp(['训练集数据的R2为：', num2str(R1)])
disp(['测试集数据的R2为：', num2str(R2)])

% 平均绝对误差 MAE
mae1 = sum(abs(T_sim1 - T_train)) ./ M ;
mae2 = sum(abs(T_sim2 - T_test )) ./ N ;

disp(['训练集数据的MAE为：', num2str(mae1)])
disp(['测试集数据的MAE为：', num2str(mae2)])

% 平均相对误差 MBE
mbe1 = sum(T_sim1 - T_train) ./ M ;
mbe2 = sum(T_sim2 - T_test ) ./ N ;

disp(['训练集数据的MBE为：', num2str(mbe1)])
disp(['测试集数据的MBE为：', num2str(mbe2)])

References

[1] https://blog.csdn.net/kjm13182345320/article/details/129215161
[2] https://blog.csdn.net/kjm13182345320/article/details/128105718