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

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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；

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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)