粒子群算法(PSO)优化长短期记忆神经网络的数据回归预测，PSO-LSTM回归预测，多输入单输出模型

lear all;
close all;
clc;

%%  导入数据
P_train = xlsread('data','training set','B2：G191')';
T_train= xlsread('data','training set','H2：H191')';
% 测试集——44个样本
P_test=xlsread('data','test set','B2:G45')';
T_test=xlsread('data','test set','H2:H45')';
%% WOA优化参数设置
SearchAgents = 10; % 种群数量  50
Function_name='LSTM_MIN'; 
Max_iterations = 10; % 迭代次数   10
lowerbound = [10 0.001 10 ];%三个参数的下限
upperbound = [200 0.02 200 ];%三个参数的上限
dimension = 3;%数量，即要优化的LSTM参数个数
fitness = @(x)LSTM_MIN(x,P_train,P_test,T_train,T_test);
%% WOA优化LSTM
[Best_score,Best_pos,Convergence_curve]=PSO(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,fitness)

PSO-LSTM完整代码获取链接