灰狼算法优化时间卷积双向门控循环单元融合注意力机制GWO-TCN-BiGRU-Attention预测附matlab代码.rar

clc; clear close all addpath(genpath(pwd)) X = xlsread('风电场预测.xlsx'); X = X(5665:8640,:); %选取3月份数据 num_samples = length(X); % 样本个数 kim = 10; % 延时步长（kim个历史数据作为自变量） zim = 1; % 跨zim个时间点进行预测 or_dim = size(X,2); % 重构数据集 for i = 1: num_samples - kim - zim + 1 res(i, :) = [reshape(X(i: i + kim - 1,:), 1, kim*or_dim), X(i + kim + zim - 1,:)]; end % 训练集和测试集划分 outdim = 1; % 最后一列为输出 num_size = 0.9; % 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 %% 优化算法优化前，构建优化前的TCN_BiGRU_Attention模型 outputSize = 1; %数据输出y的维度 numFilters = 64; filterSize = 5; dropoutFactor = 0.1; numBlocks = 2; layer = sequenceInputLayer(f_,Normalization="rescale-symmetric",Name="input"); lgraph = layerGraph(layer); outputName = layer.Name; for i = 1:numBlocks dilationFactor = 2^(i-1); layers = [ convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal",Name="conv1_"+i) layerNormalizationLayer dropoutLayer(dropoutFactor) % spatialDropoutLayer(dropoutFactor) convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal") layerNormalizationLayer reluLayer dropoutLayer(dropoutFactor) additionLayer(2,Name="add_"+i)]; % Add and connect layers. lgraph = addLayers(lgraph,layers); lgraph = connectLayers(lgraph,outputName,"conv1_"+i); % Skip connection. if i == 1 % Include convolution in first skip connection. layer = convolution1dLayer(1,numFilters,Name="convSkip"); lgraph = addLayers(lgraph,layer); lgraph = connectLayers(lgraph,outputName,"convSkip"); lgraph = connectLayers(lgraph,"convSkip","add_" + i + "/in2"); else lgraph = connectLayers(lgraph,outputName,"add_" + i + "/in2"); end % Update layer output name. outputName = "add_" + i; end tempLayers = flattenLayer("Name","flatten"); lgraph = addLayers(lgraph,tempLayers); tempLayers = gruLayer(10,"Name","gru1"); lgraph = addLayers(lgraph,tempLayers); tempLayers = [ FlipLayer("flip3") gruLayer(10,"Name","gru2")]; lgraph = addLayers(lgraph,tempLayers); tempLayers = [ concatenationLayer(1,2,"Name","concat") selfAttentionLayer(1,50,"Name","selfattention") %单头注意力Attention机制，把1改为2,3,4……即为多头，后面的50是键值 fullyConnectedLayer(outdim,"Name","fc") regressionLayer("Name","regressionoutput")]; lgraph = addLayers(lgraph,tempLayers); lgraph = connectLayers(lgraph,outputName,"flatten"); lgraph = connectLayers(lgraph,"flatten","gru1"); lgraph = connectLayers(lgraph,"flatten","flip3"); lgraph = connectLayers(lgraph,"gru1","concat/in1"); lgraph = connectLayers(lgraph,"gru2","concat/in2"); % 参数设置 options0 = trainingOptions('adam', ... % 优化算法Adam 'MaxEpochs', 60, ... % 最大训练次数 'GradientThreshold', 1, ... % 梯度阈值 'InitialLearnRate', 0.01, ... % 初始学习率 'L2Regularization', 0.0001, ... % 正则化参数 'ExecutionEnvironment', 'cpu',... % 训练环境 'Verbose', 1, ... % 关闭优化过程 'Plots', 'none'); % 画出曲线 % 网络训练 tic net0 = trainNetwork(vp_train,vt_train,lgraph,options0); toc %% 测试与评估 t_sim = net0.predict(vp_test); analyzeNetwork(net0);% 查看网络结构 % 数据反归一化 T_sim = mapminmax('reverse', t_sim, ps_output); % 数据格式转换 T_sim = cell2mat(T_sim); T_sim = T_sim'; disp(' ') disp('优化TCN_BiGRU_attention神经网络：') popsize=2; %初始种群规模 maxgen=10; %最大进化代数 fobj = @(x)objectiveFunction(x,f_,vp_train,vt_train,vp_test,T_test,ps_output); % GWO优化参数设置 lb = [0.001 10 2 0.0001]; %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, 正则化参数 ub = [0.01 50 50 0.001]; %参数的上限 dim = length(lb);%数量 [GWOt_score,GWOt_pos,GWO_curve]=GWO(popsize,maxgen,lb,ub,dim,fobj);%%修改函数名字即可完成优化算法的替换 setdemorandstream(pi); %% 绘制进化曲线 figure plot(GWO_curve,'r-','linewidth',2) xlabel('进化代数') ylabel('均方误差') legend('最佳适应度') title('优化算法进化曲线') %% 把最佳参数GWOt_pos回带 [~,optimize_T_sim] = objectiveFunction(GWOt_pos,f_,vp_train,vt_train,vp_test,T_test,ps_output); setdemorandstream(pi); %% 比较算法预测值 str={'真实值','TCN-BiGRU-Attention','优化后TCN-BiGRU-Attention'}; figure('Units', 'pixels', ... 'Position', [300 300 860 370]); plot(T_test,'-','Color',[0.8500 0.3250 0.0980]) hold on plot(T_sim,'-.','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_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]; 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','TCN-BiGRU-Attention','优化后TCN-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 0.8176 0.5569 0.8118 0.7882 1.0000 0.5333 0.5176]; figure('Units', 'pixels', ... 'Position', [300 300 660 375]); plot_data_t=Test_all(:,[1,2,4])'; b=bar(plot_data_t,0.8); hold on for i = 1 : size(plot_data_t,2) x_data(:, i) = b(i).XEndPoints'; end for i =1:size(plot_data_t,2) b(i).FaceColor = color(i,:); b(i).EdgeColor=[0.3353 0.3314 0.6431]; b(i).LineWidth=1.2; end for i = 1 : size(plot_data_t,1)-1 xilnk=(x_data(i, end)+ x_data(i+1, 1))/2; b1=xline(xilnk,'--','LineWidth',1.2); hold on end ax=gca; legend(b,str1,'Location','GWOt') ax.XTickLabels ={'MAE', 'MAPE', 'RMSE'}; set(gca,"FontSize",10,"LineWidth",1) box off legend box off %% 二维图 figure plot_data_t1=Test_all(:,[1,5])'; MarkerType={'s','o','pentagram','^','v'};