基于PSO粒子群优化的CNN-LSTM的时间序列回归预测matlab仿真

1.算法运行效果图预览

2.算法运行软件版本

3.部分核心程序

4.算法理论概述

4.1 卷积神经网络（CNN）

4.2 长短期记忆网络（LSTM）

CNN-LSTM%E7%BB%93%E5%90%88PSO%E7%9A%84%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97%E9%A2%84%E6%B5%8B-toc" style="margin-left:40px;">4.3 CNN-LSTM结合PSO的时间序列预测

5.算法完整程序工程

1.算法运行效果图预览

2.算法运行软件版本

matlab2022a

3.部分核心程序

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for i=1:Iterifor j=1:Npeoprng(i+j)if func_obj(x1(j,:))<pbest1(j)p1(j,:)   = x1(j,:);%变量pbest1(j) = func_obj(x1(j,:));endif pbest1(j)<gbest1g1     = p1(j,:);%变量gbest1 = pbest1(j);endv1(j,:) = 0.8*v1(j,:)+c1*rand*(p1(j,:)-x1(j,:))+c2*rand*(g1-x1(j,:));x1(j,:) = x1(j,:)+v1(j,:); for k=1:dimsif x1(j,k) >= tmps(2,k)x1(j,k) = tmps(2,k);endif x1(j,k) <= tmps(1,k)x1(j,k) = tmps(1,k);endendfor k=1:dimsif v1(j,k) >= tmps(2,k)/2v1(j,k) =  tmps(2,k)/2;endif v1(j,k) <= tmps(1,k)/2v1(j,k) =  tmps(1,k)/2;endendendgb1(i)=gbest1 
endfigure;
plot(gb1,'-bs',...'LineWidth',1,...'MarkerSize',6,...'MarkerEdgeColor','k',...'MarkerFaceColor',[0.9,0.0,0.0]);xlabel('优化迭代次数');
ylabel('适应度值');
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figure;
plot(IT(1:1:end),Accuracy(1:1:end));
xlabel('epoch');
ylabel('RMSE');
%数据预测
Dpre1 = predict(Net, Nsp_train2);
Dpre2 = predict(Net, Nsp_test2);%归一化还原
T_sim1=Dpre1*Vmax2;
T_sim2=Dpre2*Vmax2;%网络结构
analyzeNetwork(Net)figure
subplot(211);
plot(1: Num1, Tat_train,'-bs',...'LineWidth',1,...'MarkerSize',6,...'MarkerEdgeColor','k',...'MarkerFaceColor',[0.9,0.0,0.0]);
hold on
plot(1: Num1, T_sim1,'g',...'LineWidth',2,...'MarkerSize',6,...'MarkerEdgeColor','k',...'MarkerFaceColor',[0.9,0.9,0.0]);legend('真实值', '预测值')
xlabel('预测样本')
ylabel('预测结果')
grid onsubplot(212);
plot(1: Num1, Tat_train-T_sim1','-bs',...'LineWidth',1,...'MarkerSize',6,...'MarkerEdgeColor','k',...'MarkerFaceColor',[0.9,0.0,0.0]);xlabel('预测样本')
ylabel('预测误差')
grid on
ylim([-50,50]);figure
subplot(211);
plot(1: Num2, Tat_test,'-bs',...'LineWidth',1,...'MarkerSize',6,...'MarkerEdgeColor','k',...'MarkerFaceColor',[0.9,0.0,0.0]);
hold on
plot(1: Num2, T_sim2,'g',...'LineWidth',2,...'MarkerSize',6,...'MarkerEdgeColor','k',...'MarkerFaceColor',[0.9,0.9,0.0]);
legend('真实值', '预测值')
xlabel('测试样本')
ylabel('测试结果')
grid onsubplot(212);
plot(1: Num2, Tat_test-T_sim2','-bs',...'LineWidth',1,...'MarkerSize',6,...'MarkerEdgeColor','k',...'MarkerFaceColor',[0.9,0.0,0.0]);xlabel('预测样本')
ylabel('预测误差')
grid on
ylim([-50,50]);save R2.mat Num2 Tat_test T_sim2 gb1 Accuracy
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4.算法理论概述

基于粒子群优化（Particle Swarm Optimization, PSO）的卷积神经网络-长短期记忆网络（Convolutional Neural Network - Long Short-Term Memory, CNN-LSTM）模型在时间序列回归预测中，结合了深度学习的强大表达能力和优化算法的高效搜索能力，为复杂时间序列数据的预测提供了一种强有力的解决方案。