【LSTM回归预测】基于粒子群算法优化长短记忆神经网络PSO-LSTM实现台风风电功率多输入单输出预测附matlab代码.rar

%% 粒子群优化LSTM(PSO_LSTM) clc clear close all %% 数据读取 geshu=200;%训练集的个数 %读取数据 shuru=xlsread('input.xlsx'); shuchu=xlsread('output.xlsx'); nn = randperm(size(shuru,1));%随机排序 % nn=1:size(shuru,1);%正常排序 input_train =shuru(nn(1:geshu),:); input_train=input_train'; output_train=shuchu(nn(1:geshu),:); output_train=output_train'; input_test =shuru(nn((geshu+1):end),:); input_test=input_test'; output_test=shuchu(nn((geshu+1):end),:); output_test=output_test'; %样本输入输出数据归一化 [aa,bb]=mapminmax([input_train input_test]); [cc,dd]=mapminmax([output_train output_test]); global inputn outputn shuru_num shuchu_num XValidation YValidation [inputn,inputps]=mapminmax('apply',input_train,bb); [outputn,outputps]=mapminmax('apply',output_train,dd); shuru_num = size(input_train,1); % 输入维度 shuchu_num = 1; % 输出维度 dam = 10; % 验证集个数，验证集是从训练集里面取的数据 idx = randperm(size(inputn,2),dam); XValidation = inputn(:,idx); inputn(:,idx) = []; YValidation = outputn(idx); outputn(idx) = []; YValidationy = output_train(idx); output_train(idx) = []; %% % 1. 寻找最佳参数 NN=5; %初始化群体个数 D=2; %初始化群体维数， T=10; %初始化群体最迭代次数 c1=2; %学习因子1 c2=2; %学习因子2 %用线性递减因子粒子群算法 Wmax=1.2; %惯性权重最大值 Wmin=0.8; %惯性权重最小值 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %每个变量的取值范围 ParticleScope(1,:)=[10 200]; % 中间层神经元个数 ParticleScope(2,:)=[0.01 0.15]; % 学习率 ParticleScope=ParticleScope'; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% xv=rand(NN,2*D); %首先，初始化种群个体速度和位置 for d=1:D xv(:,d)=xv(:,d)*(ParticleScope(2,d)-ParticleScope(1,d))+ParticleScope(1,d); xv(:,D+d)=(2*xv(:,D+d)-1 )*(ParticleScope(2,d)-ParticleScope(1,d))*0.2; end x1=xv(:,1:D);%位置 v1=xv(:,D+1:2*D);%速度 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %------初始化个体位置和适应度值----------------- p1=x1; pbest1=ones(NN,1); for i=1:NN pbest1(i)=fitness(x1(i,:)); end %------初始时全局最优位置和最优值--------------- gbest1=min(pbest1); lab=find(min(pbest1)==pbest1); g1=x1(lab,:); gb1=ones(1,T); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %-----浸入主循环，按照公式依次迭代直到迭代次数--- % N=40; %初始化群体个数 % D=10; %初始化群体维数 % T=100; %初始化群体最迭代次数 for i=1:T for j=1:NN if (fitness(x1(j,:))<pbest1(j)) p1(j,:)=x1(j,:);%变量 pbest1(j)=fitness(x1(j,:)); end if(pbest1(j)<gbest1) g1=p1(j,:);%变量 gbest1=pbest1(j); end w=Wmax-(Wmax-Wmin)*i/T; v1(j,:)=w*v1(j,:)+c1*rand*(p1(j,:)-x1(j,:))+c2*rand*(g1-x1(j,:)); x1(j,:)=x1(j,:)+v1(j,:); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%位置约束 label=find(x1(j,:)>ParticleScope(2,:)); x1(j,label)=ParticleScope(2,label); label2=find(x1(j,:)<ParticleScope(1,:)); x1(j,label2)=ParticleScope(1,label2); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%速度约束 labe3=find(v1(j,:)>ParticleScope(2,:)*0.2); v1(j,labe3)=ParticleScope(2,labe3)*0.2; label4=find(v1(j,:)<-ParticleScope(2,:)*0.2); v1(j,label4)=-ParticleScope(2,label4)*0.2; end % gb1(i)=min(pbest1); gb1(i)=gbest1; end zhongjian1_num = round(g1(1)); xue = g1(2); %% 模型建立与训练 layers = [ ... sequenceInputLayer(shuru_num) % 输入层 lstmLayer(zhongjian1_num) % LSTM层 fullyConnectedLayer(shuchu_num) % 全连接层 regressionLayer]; options = trainingOptions('adam', ... % 梯度下降 'MaxEpochs',50, ... % 最大迭代次数 'GradientThreshold',1, ... % 梯度阈值 'InitialLearnRate',xue,... 'Verbose',0, ... 'Plots','training-progress','ValidationData',{XValidation,YValidation}); %% 训练LSTM net = trainNetwork(inputn,outputn,layers,options); %% 预测 net = resetState(net);% 网络的更新状态可能对分类产生了负面影响。重置网络状态并再次预测序列。 [~,Ytrain]= predictAndUpdateState(net,inputn); test_simu=mapminmax('reverse',Ytrain,dd);%反归一化 %测试集样本输入输出数据归一化 inputn_test=mapminmax('apply',input_test,bb); [net,an]= predictAndUpdateState(net,inputn_test); test_simu1=mapminmax('reverse',an,dd);%反归一化 error1=test_simu1-output_test;%测试集预测-真实 [~,Ytrain]= predictAndUpdateState(net,XValidation); test_simuy=mapminmax('reverse',Ytrain,dd);%反归一化 %% 画图 figure plot(output_train,'r-o','Color',[255 0 0]./255,'linewidth',0.8,'Markersize',4,'MarkerFaceColor',[255 0 0]./255) hold on plot(test_simu,'-s','Color',[0 0 0]./255,'linewidth',0.8,'Markersize',5,'MarkerFaceColor',[0 0 0]./255) hold off legend(["真实值" "预测值"]) xlabel("样本") title("训练集") figure plot(YValidationy,'-o','Color',[255 255 0]./255,'linewidth',0.8,'Markersize',4,'MarkerFaceColor',[255 0 0]./255) hold on plot(test_simuy,'-s','Color',[0 0 0]./255,'linewidth',0.8,'Markersize',5,'MarkerFaceColor',[0 0 0]./255) hold off legend(["真实值" "预测值"]) xlabel("样本") title("验证集") figure plot(output_test,'-o','Color',[0 0 255]./255,'linewidth',0.8,'Markersize',4,'MarkerFaceColor',[25 0 255]./255) hold on plot(test_simu1,'-s','Color',[0 0 0]./255,'linewidth',0.8,'Markersize',5,'MarkerFaceColor',[0 0 0]./255) hold off legend(["真实值" "预测值"]) xlabel("样本") title("测试集") % 真实数据，行数代表特征数，列数代表样本数output_test = output_test; T_sim_optimized = test_simu1; % 仿真数据 num=size(output_test,2);%统计样本总数 error=T_sim_optimized-output_test; %计算误差 mae=sum(abs(error))/num; %计算平均绝对误差 me=sum((error))/num; %计算平均绝对误差 mse=sum(error.*error)/num; %计算均方误差 rmse=sqrt(mse); %计算均方误差根 % R2=r*r; tn_sim = T_sim_optimized'; tn_test =output_test'; N = size(tn_test,1); R2=(N*sum(tn_sim.*tn_test)-sum(tn_sim)*sum(tn_test))^2/((N*sum((tn_sim).^2)-(sum(tn_sim))^2)*(N*sum((tn_test).^2)-(sum(tn_test))^2)); disp(' ') disp('----------------------------------------------------------') disp(['平均绝对误差mae为： ',num2str(mae)]) disp(['平均误差me为： ',num2str(me)]) disp(['均方误差根rmse为： ',num2str(rmse)]) disp(['相关系数R2为： ' ,num2str(R2)])