多维时序 | Matlab实现DBO-BiLSTM蜣螂算法优化双向长短期记忆神经网络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-BiLSTM多变量时间序列预测，蜣螂算法优化双向长短期记忆神经网络；
蜣螂算法优化优化BiLSTM的学习率，隐藏层节点，正则化系数；
2.运行环境为Matlab2018b；
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-BiLSTM蜣螂算法优化双向长短期记忆神经网络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_);[Best_score,Best_pos,Convergence_curve]=GA(SearchAgents_no,Max_iteration,lb ,ub,dim,fitness)%%  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bilstmLayer(best_hd)              % BiLSTM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;elsea=-1;endx( i , : ) =  pX(  i , :)+0.3*abs(pX(i , : )-worse)+a*0.1*(XX( i , :)); % Equation (1)elseaaa= randperm(180,1);if ( aaa==0 ||aaa==90 ||aaa==180 )x(  i , : ) = pX(  i , :);   endtheta= aaa*pi/180;   x(  i , : ) = pX(  i , :)+tan(theta).*abs(pX(i , : )-XX( i , :));    % Equation (2)      endx(  i , : ) = Bounds( x(i , : ), lb, ub );    fit(  i  ) = fobj( x(i , : ) );end [ fMMin, bestII ] = min( fit );      % fMin denotes the current optimum fitness valuebestXX = 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)