多维时序|Matlab实现DBO-GRU蜣螂算法优化门控循环单元多变量时间序列预测

多维时序 | Matlab实现DBO-GRU蜣螂算法优化门控循环单元多变量时间序列预测

目录 多维时序 | Matlab实现DBO-GRU蜣螂算法优化门控循环单元多变量时间序列预测效果一览基本介绍程序设计参考资料

效果一览

基本介绍

1.Matlab实现DBO-GRU蜣螂算法优化门控循环单元多变量时间序列预测； 蜣螂算法优化GRU的学习率，隐藏层节点，正则化系数； 2.运行环境为Matlab2020b； 3.输入多个特征，输出单个变量，考虑历史特征的影响，多变量时间序列预测； 4.data为数据集，main.m为主程序，运行即可,所有文件放在一个文件夹； 5.命令窗口输出R2、MSE、MAE、MAPE和MBE多指标评价；

程序设计 完整程序和数据下载方式资源处下载：Matlab实现DBO-GRU蜣螂算法优化门控循环单元多变量时间序列预测。 %% 优化算法参数设置 SearchAgents_no = 5; % 种群数量 Max_iteration = 8; % 最大迭代次数 dim = 3; % 优化参数个数 lb = [1e-4, 10, 1e-4]; % 参数取值下界(学习率，隐藏层节点，正则化系数) ub = [1e-2, 30, 1e-1]; % 参数取值上界(学习率，隐藏层节点，正则化系数) fitness = @(x)fical(x,p_train,t_train,f_); %% 记录最佳参数 Best_pos(1, 2) = round(Best_pos(1, 2)); best_lr = Best_pos(1, 1); best_hd = Best_pos(1, 2); best_l2 = Best_pos(1, 3); %% 建立模型 % ---------------------- 修改模型结构时需对应修改fical.m中的模型结构 -------------------------- layers = [ sequenceInputLayer(f_) % 输入层 reluLayer % Relu激活层 fullyConnectedLayer(outdim) % 输出回归层 regressionLayer]; %% 参数设置 % ---------------------- 修改模型参数时需对应修改fical.m中的模型参数 -------------------------- options = trainingOptions('adam', ... % Adam 梯度下降算法 'MaxEpochs', 500, ... % 最大训练次数 500 'InitialLearnRate', best_lr, ... % 初始学习率 best_lr 'LearnRateSchedule', 'piecewise', ... % 学习率下降 'LearnRateDropFactor', 0.5, ... % 学习率下降因子 0.1 'LearnRateDropPeriod', 400, ... % 经过 400 次训练后 学习率为 best_lr * 0.5 'Shuffle', 'every-epoch', ... % 每次训练打乱数据集 'ValidationPatience', Inf, ... % 关闭验证 'L2Regularization', best_l2, ... % 正则化参数 'Plots', 'training-progress', ... % 画出曲线 'Verbose', false); %% 训练模型 net = trainNetwork(p_train, t_train, layers, options); %% 仿真验证 t_sim1 = predict(net, p_train); t_sim2 = predict(net, p_test ); %% 数据反归一化 T_sim1 = mapminmax('reverse', t_sim1, ps_output); T_sim2 = mapminmax('reverse', t_sim2, ps_output); T_sim1=double(T_sim1); T_sim2=double(T_sim2); pFit = fit; pX = x; XX=pX; [ fMin, bestI ] = min( fit ); % fMin denotes the global optimum fitness value bestX = x( bestI, : ); % bestX denotes the global optimum position corresponding to fMin % Start updating the solutions. for t = 1 : M [fmax,B]=max(fit); worse= x(B,:); r2=rand(1); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i = 1 : pNum if(r2<0.9) r1=rand(1); a=rand(1,1); if (a>0.1) a=1; else a=-1; end x( i , : ) = pX( i , :)+0.3*abs(pX(i , : )-worse)+a*0.1*(XX( i , :)); % Equation (1) else aaa= randperm(180,1); if ( aaa==0 ||aaa==90 ||aaa==180 ) x( i , : ) = pX( i , :); end theta= aaa*pi/180; x( i , : ) = pX( i , :)+tan(theta).*abs(pX(i , : )-XX( i , :)); % Equation (2) end x( i , : ) = Bounds( x(i , : ), lb, ub ); fit( i ) = fobj( x(i , : ) ); end [ fMMin, bestII ] = min( fit ); % fMin denotes the current optimum fitness value bestXX = x( bestII, : ); % bestXX denotes the current optimum position R=1-t/M; % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Xnew1 = bestXX.*(1-R); Xnew2 =bestXX.*(1+R); %%% Equation (3) Xnew1= Bounds( Xnew1, lb, ub ); Xnew2 = Bounds( Xnew2, lb, ub ); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Xnew11 = bestX.*(1-R); Xnew22 =bestX.*(1+R); %%% Equation (5) Xnew11= Bounds( Xnew11, lb, ub ); Xnew22 = Bounds( Xnew22, lb, ub ); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i = ( pNum + 1 ) :12 % Equation (4) 参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/129215161 [2] https://blog.csdn.net/kjm13182345320/article/details/128105718