基于鲸鱼算法优化双向长短期记忆网络结合注意力机制WOA-BILSTM-Attention回归预测，多变量输入模型 matlab代

%% 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 % 清空命令行 warning('off') %% 导入数据 res = xlsread('数据集.xlsx'); %% 数据分析 num_size = 0.7; % 训练集占数据集比例 outdim = 1; % 最后一列为输出 num_samples = size(res, 1); % 样本个数 res = res(randperm(num_samples), :); % 打乱数据集（不希望打乱时，注释该行） 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 %% 划分训练集和测试集 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 %% 创建待优化函数 ObjFcn = @CostFunction; %% 优化参数设置 SearchAgents = 10; % 种群数量 Max_iterations = 15 ; % 迭代次数 lowerbound = [1e-8 0.0001 10 ]; % 三个参数的下限 upperbound = [1e-2 0.002 100 ]; % 三个参数的上限 dimension = 3; % 数量，即要优化的参数个数 %%优化 [Best_score,Best_pos,Convergence_curve] = WOA(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,ObjFcn); %% 得到最优参数 NumOfUnits = round( Best_pos(1,3)); % 最佳隐藏层节点数 InitialLearnRate = abs(Best_pos(1,2)); % 最佳初始学习率 L2Regularization = abs(Best_pos(1,1)); % 最佳L2正则化系数 %% 建立模型 lgraph = layerGraph(); % 建立空白网络结构 layers = [ globalAveragePooling2dLayer("Name","gapool") % 全局平均池化层 fullyConnectedLayer(16, "Name", "fc_2") % SE注意力机制， 通道数的1/4 reluLayer("Name", "relu_3") % Relu 激活层 fullyConnectedLayer(64, "Name", "fc_3") % SE注意力机制， 通道数和数目相同 sigmoidLayer("Name", "sigmoid")]; % sigmoid 激活层 lgraph = addLayers(lgraph, layers); % 将上述网络结构加入空白结构中 layers = multiplicationLayer(2, "Name", "multiplication"); % 点乘注意力机制 lgraph = addLayers(lgraph, layers); % 将上述网络结构加入空白结构中 layers = [ sequenceInputLayer(f_) % 输入层,即输入的特征变量个数 bilstmLayer(NumOfUnits) % BILSTM层 reluLayer % Relu激活层 fullyConnectedLayer(outdim) % 全连接层 regressionLayer]; % 回归层 lgraph = addLayers(lgraph, layers); % 将上述网络结构加入空白结构中 %% 参数设置 options = trainingOptions('adam', ... % 优化算法Adam 'MaxEpochs', 500, ... % 最大训练次数 'GradientThreshold', 1, ... % 梯度阈值 'InitialLearnRate', InitialLearnRate, ... % 初始学习率 'LearnRateSchedule', 'piecewise', ... % 学习率调整 'LearnRateDropPeriod', 400, ... % 训练850次后开始调整学习率 'LearnRateDropFactor',0.2, ... % 学习率调整因子 'L2Regularization', L2Regularization, ... % 正则化参数 'ExecutionEnvironment', 'cpu',... % 训练环境 'Verbose', 0, ... % 关闭优化过程 'Plots', 'training-progress'); % 画出曲线 %% 训练 net = trainNetwork(vp_train, vt_train, layers, options); %% 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); %% 数据格式转换 T_sim1 = cell2mat(T_sim1); T_sim2 = cell2mat(T_sim2); T_sim1 = double(T_sim1); T_sim2 = double(T_sim2); %% 绘制迭代图 figure plot(Convergence_curve,'linewidth',1.5); grid on xlabel('迭代次数') ylabel('适应度值') title('迭代曲线图') grid on set(gcf,'color','w') %% 测试集结果 figure; plotregression(T_test,T_sim2,['回归图']); figure; ploterrhist(T_test-T_sim2,['误差直方图']); %% 均方根误差 RMSE error1 = sqrt(sum((T_sim1 - T_train).^2)./M); error2 = sqrt(sum((T_test - T_sim2).^2)./N); %% %决定系数 R1 = 1 - norm(T_train - T_sim1)^2 / norm(T_train - mean(T_train))^2; R2 = 1 - norm(T_test - T_sim2)^2 / norm(T_test - mean(T_test ))^2; %% %均方误差 MSE mse1 = sum((T_sim1 - T_train).^2)./M; mse2 = sum((T_sim2 - T_test).^2)./N; %% %RPD 剩余预测残差 SE1=std(T_sim1-T_train); RPD1=std(T_train)/SE1; SE=std(T_sim2-T_test); RPD2=std(T_test)/SE; %% 平均绝对误差MAE MAE1 = mean(abs(T_train - T_sim1)); MAE2 = mean(abs(T_test - T_sim2)); %% 平均绝对百分比误差MAPE MAPE1 = mean(abs((T_train - T_sim1)./T_train)); MAPE2 = mean(abs((T_test - T_sim2)./T_test)); %% 训练集绘图 figure %plot(1:M,T_train,'r-*',1:M,T_sim1,'b-o','LineWidth',1) plot(1:M,T_train,'r-*',1:M,T_sim1,'b-o','LineWidth',1.5) legend('真实值','WOA-BILSTM-attention预测值') xlabel('预测样本') ylabel('预测结果') string={'训练集预测结果对比';['(R^2 =' num2str(R1) ' RMSE= ' num2str(error1) ' MSE= ' num2str(mse1) ' RPD= ' num2str(RPD1) ')' ]}; title(string) %% 预测集绘图 figure plot(1:N,T_test,'r-*',1:N,T_sim2,'b-o','LineWidth',1.5) legend('真实值','WOA-BILSTM-attention预测值') xlabel('预测样本') ylabel('预测结果') string={'测试集预测结果对比';['(R^2 =' num2str(R2) ' RMSE= ' num2str(error2) ' MSE= ' num2str(mse2) ' RPD= ' num2str(RPD2) ')']}; title(string) %% 测试集误差图 figure ERROR3=T_test-T_sim2; plot(T_test-T_sim2,'b-*','LineWidth',1.5) xlabel('测试集样本编号') ylabel('预测误差') title('测试集预测误差') grid on; legend('WOA-BILSTM-attention预测输出误差') %% 绘制线性拟合图 figure bar((T_sim2 - T_test )./T_test) legend('相对误差') title('相对误差') ylabel('误差') xlabel('数量') xlim([1 N]); %% 训练集拟合效果图 figure plot(T_train,T_sim1,'*r'); xlabel('真实值') ylabel('预测值') string = {'训练集效果图';['R^2_c=' num2str(R1) ' RMSEC=' num2str(error1) ]}; title(string) hold on ;h=lsline; set(h,'LineWidth',1,'LineStyle','-','Color',[1 0 1]) %% 预测集拟合效果图 figure plot(T_test,T_sim2,'ob'); xlabel('真实值') ylabel('预测值') string1 = {'测试集效果图';['R^2_p=' num2str(R2) ' RMSEP=' num2str(error2) ]}; title(string1) hold on ;h=lsline(); set(h,'LineWidth',1,'LineStyle','-','Color',[1 0 1]) figure bar((T_sim2 - T_test )./T_test) legend('相对误差') title('相对误差') ylabel('误差') xlabe