12种算法优化CNN-BiGRU-Attention.rar

%% 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 %% 导入数据 addpath(genpath(pwd)); data = readmatrix('风电场预测.xlsx'); data = data(5665:8640,12); %选取3月份数据 [h1,l1]=data_process(data,24); %步长为24，采用前24个时刻的温度预测第25个时刻的温度 res = [h1,l1]; num_samples = size(res,1); %样本个数 % 训练集和测试集划分 outdim = 1; % 最后一列为输出 num_size = 0.7; % 训练集占数据集比例 num_train_s = round(num_size * num_samples); % 训练集样本个数 f_ = size(res, 2) - outdim; % 输入特征维度 P_train = res(1: num_train_s, 1: f_)'; T_train = res(1: num_train_s, f_ + 1: end)'; M = size(P_train, 2); P_test = res(num_train_s + 1: end, 1: f_)'; T_test = res(num_train_s + 1: end, f_ + 1: end)'; 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); % 格式转换 for i = 1 : M vp_train{i, 1} = p_train(:, i); vt_train{i, 1} = t_train(:, i); end for i = 1 : N vp_test{i, 1} = p_test(:, i); vt_test{i, 1} = t_test(:, i); end numFeatures = size(p_train,1); %% 网络搭建CNN-BiGRU-ATTENTION lgraph = layerGraph(); % 添加层分支 % 将网络分支添加到层次图中。每个分支均为一个线性层组。 tempLayers = sequenceInputLayer([numFeatures,1,1],"Name","sequence"); lgraph = addLayers(lgraph,tempLayers); tempLayers = [ convolution2dLayer([3,1],16,"Name","conv","Padding","same") batchNormalizationLayer("Name","batchnorm") reluLayer("Name","relu") maxPooling2dLayer([2 2],"Name","maxpool","Padding","same") convolution2dLayer([3,1],16,"Name","conv2","Padding","same") batchNormalizationLayer("Name","batchnorm2") reluLayer("Name","relu2") maxPooling2dLayer([2 2],"Name","maxpool2","Padding","same") flattenLayer("Name","flatten_1") fullyConnectedLayer(25,"Name","fc_1")]; lgraph = addLayers(lgraph,tempLayers); tempLayers = flattenLayer("Name","flatten"); lgraph = addLayers(lgraph,tempLayers); tempLayers = gruLayer(35,"Name","gru1"); lgraph = addLayers(lgraph,tempLayers); tempLayers = [ FlipLayer("flip3") gruLayer(35,"Name","gru2")]; lgraph = addLayers(lgraph,tempLayers); tempLayers = [ concatenationLayer(1,3,"Name","concat") selfAttentionLayer(1,50,"Name","selfattention") %Attention机制 fullyConnectedLayer(outdim,"Name","fc") regressionLayer("Name","regressionoutput")]; lgraph = addLayers(lgraph,tempLayers); % 清理辅助变量 clear tempLayers; % 连接层分支 % 连接网络的所有分支以创建网络图。 lgraph = connectLayers(lgraph,"sequence","conv"); lgraph = connectLayers(lgraph,"sequence","flatten"); lgraph = connectLayers(lgraph,"flatten","gru1"); lgraph = connectLayers(lgraph,"flatten","flip3"); lgraph = connectLayers(lgraph,"gru1","concat/in1"); lgraph = connectLayers(lgraph,"gru2","concat/in2"); lgraph = connectLayers(lgraph,"fc_1","concat/in3"); %% 参数设置 options = trainingOptions('adam', ... % 优化算法Adam 'MaxEpochs', 30, ... % 最大训练次数 'GradientThreshold', 1, ... % 梯度阈值 'InitialLearnRate', 0.01, ... % 初始学习率 'LearnRateSchedule', 'piecewise', ... % 学习率调整 'LearnRateDropPeriod', 22, ... % 训练60次后开始调整学习率 'LearnRateDropFactor',0.1, ... % 学习率调整因子 'ExecutionEnvironment', 'cpu',... % 训练环境 'Verbose', 1, ... % 关闭优化过程 'Plots', 'none'); % 画出曲线 % 训练 tic net = trainNetwork(vp_train, vt_train, lgraph, options); toc analyzeNetwork(net);% 查看网络结构 % 预测 t_sim1 = predict(net, vp_train); t_sim2 = predict(net, vp_test); % 数据反归一化 T_sim1 = mapminmax('reverse', t_sim1, ps_output); T_sim2 = mapminmax('reverse', t_sim2, ps_output); T_train1 = T_train; T_test2 = T_test; % 数据格式转换 T_sim1 = cell2mat(T_sim1);% cell2mat将cell元胞数组转换为普通数组 T_sim2 = cell2mat(T_sim2); T_sim1 = T_sim1'; T_sim2 = T_sim2'; CNN_BiGRU_ATTENTION_TSIM1 = T_sim1; CNN_BiGRU_ATTENTION_TSIM2 = T_sim2; save CNN_BiGRU_ATTENTION CNN_BiGRU_ATTENTION_TSIM1 CNN_BiGRU_ATTENTION_TSIM2 % 指标计算 disp('…………训练集误差指标…………') [mae1,rmse1,mape1,error1]=calc_error(T_train1,T_sim1); fprintf('\n') figure('Position',[200,300,600,200]) plot(T_train1); hold on plot(T_sim1) legend('真实值','预测值') title('CNN-BiGRU-ATTENTION训练集预测效果对比') xlabel('样本点') ylabel('发电功率') disp('…………测试集误差指标…………') [mae2,rmse2,mape2,error2]=calc_error(T_test2,T_sim2); fprintf('\n') figure('Position',[200,300,600,200]) plot(T_test2); hold on plot(T_sim2) legend('真实值','预测值') title('CNN-BiGRU-ATTENTION预测集预测效果对比') xlabel('样本点') ylabel('发电功率') figure('Position',[200,300,600,200]) plot(T_sim2-T_test2) title('CNN-BiGRU-ATTENTION误差曲线图') xlabel('样本点') ylabel('发电功率') %% 优化CNN-BiGRU-Attention disp(' ') disp('优化CNN_BiLSTM_attention神经网络：') %% 初始化参数 popsize=10; %初始种群规模 maxgen=8; %最大进化代数 fobj = @(x)objectiveFunction(x,numFeatures,outdim,vp_train,vt_train,vp_test,T_test,ps_output); % 优化参数设置 lb = [0.001 10 2 2]; %参数的下限。分别是学习率，biGRU的神经元个数，注意力机制的键值, 卷积核大小 ub = [0.01 50 50 10]; %参数的上限 dim = length(lb);%数量 % 可选：'DBO','GWO','OOA','PSO','SABO','SCSO','SSA','BWO','RIME','WOA','HHO','NGO'; [Best_score,Best_pos,curve]=NGO(popsize,maxgen,lb,ub,dim,fobj); %修改这里的函数名字即可 setdemorandstream(pi); %% 绘制进化曲线 figure plot(curve,'r-','linewidth',2) xlabel('进化代数') ylabel('均方误差') legend('最佳适应度') title('进化曲线') %% 把最佳参数Best_pos回带 [~,optimize_T_sim] = objectiveFunction(Best_pos,numFeatures,outdim,vp_train,vt_train,vp_test,T_test,ps_output); setdemorandstream(pi); %% 比较算法预测值 str={'真实值','CNN-BiGRU-Attention','优化后CNN-BiGRU-Attention'}; figure('Units', 'pixels', ... 'Position', [300 300 860 370]); plot(T_test,'-','Color',[0.8500 0.3250 0.0980]) hold on plot(T_sim2,'-.','Color',[0.4940 0.1840 0.5560]) hold on plot(optimize_T_sim,'-','Color',[0.4660 0.6740 0.1880]) legend(str) set (gca,"FontSize",12,'LineWidth',1.2) box off legend Box off %% 比较算法误差 test_y = T_test; Test_all = []; y_test_predict = T_sim2; [test_MAE,test_MAPE,test_MSE,test_RMSE,test_R2]=calc_error(y_test_predict,test_y); Test_all=[Test_all;test_MAE test_MAPE test_MSE test_RMSE test_R2]; y_test_predict = optimize_T_sim; [test_MAE,test_MAPE,test_MSE,test_RMSE,test_R2]=calc_error(y_test_predict,test_y); Test_all=[Test_all;test_MAE test_MAPE test_MSE test_RMSE test_R2]; str={'真实值','CNN-BiGRU-Attention','优化后CNN-BiGRU-Attention'}; str1=str(2:end); str2={'MAE','MAPE','MSE','RMSE','R2'}; data_out=array2table(Test_all); data_out.Properties.VariableNames=str2; data_out.Properties.RowNames=str1; disp(data_out) %% 柱状图 MAE MAPE RMSE 柱状图适合量纲差别不大的 color= [0.66669 0.1206 0.108; 0.1339 0.7882 0.8588;0.1525 0.6645 0.1290; 0.8549 0.9373 0.8275 ; 0.1551 0.2176 0.8627; 0.7843 0.1412 0.1373; 0.2000 0.9213 0.8