时序预测 | MATLAB实现基于PSO-BiLSTM、BiLSTM时间序列预测对比

时序预测 | MATLAB实现基于PSO-BiLSTM、BiLSTM时间序列预测对比

效果一览

基本描述

MATLAB实现基于PSO-BiLSTM、BiLSTM时间序列预测对比。
1.Matlab实现PSO-BiLSTM和BiLSTM神经网络时间序列预测；
2.输入数据为单变量时间序列数据，即一维数据；
3.运行环境Matlab2020及以上,依次运行Main1BiLSTMTS、Main2PSOBiLSTMTS、Main3CDM即可，其余为函数文件无需运行,所有程序放在一个文件夹,data为数据集；
BiLSTM（双向长短时记忆模型）与粒子群算法优化后的BiLSTM（PSOBiLSTM）对比实验，可用于风电、光伏等负荷预测，时序预测，数据为单输入单输出，PSO优化超参数为隐含层1节点数、隐含层2节点数、最大迭代次数和学习率。

命令窗口输出MAE、MAPE、RMSE和R2；

程序设计

• 完整程序和数据下载：私信博主回复MATLAB实现基于PSO-BiLSTM、BiLSTM时间序列预测对比。

for i=1:PopNum%随机初始化速度,随机初始化位置    for j=1:dim        if j==dim% % 隐含层节点与训练次数是整数 学习率是浮点型            pop(i,j)=(xmax(j)-xmin(j))*rand+xmin(j);        else            pop(i,j)=round((xmax(j)-xmin(j))*rand+xmin(j));  %        end    endend% calculate the fitness_value of Poppbest = pop;gbest = zeros(1,dim);data1 = zeros(Maxstep,PopNum,dim);data2 = zeros(Maxstep,PopNum);for i = 1:PopNum    fit(i) = fitness(pop(i,:),p_train,t_train,p_test,t_test);    f_pbest(i) = fit(i);endg = min(find(f_pbest == min(f_pbest(1:PopNum))));gbest = pbest(g,:);f_gbest = f_pbest(g);%-------- in the loop -------------for step = 1:Maxstep        mbest =sum(pbest(:))/PopNum;    % linear weigh factor    b = 1-step/Maxstep*0.5;    data1(step,:,:) = pop;    data2(step,:) = fit;    for i = 1:PopNum        a = rand(1,dim);        u = rand(1,dim);        p = a.*pbest(i,:)+(1-a).*gbest;        pop(i,:) = p + b*abs(mbest-pop(i,:)).*...            log(1./u).*(1-2*(u >= 0.5));        % boundary detection                for j=1:dim            if j ==dim                if pop(i,j)>xmax(j) | pop(i,j)<xmin(j)                    pop(i,j)=(xmax(j)-xmin(j))*rand+xmin(j);  %                end            else                pop(i,j)=round(pop(i,j));                if pop(i,j)>xmax(j) | pop(i,j)<xmin(j)                    pop(i,j)=round((xmax(j)-xmin(j))*rand+xmin(j));  %                end            end        end                        fit(i) = fitness(pop(i,:),p_train,t_train,p_test,t_test);        if fit(i) < f_pbest(i)            pbest(i,:) = pop(i,:);            f_pbest(i) = fit(i);        end        if f_pbest(i) < f_gbest            gbest = pbest(i,:);            f_gbest = f_pbest(i);        end    end    trace(step)=f_gbest;    step,f_gbest,gbest    result(step,:)=gbest;endor i=1:N%随机初始化速度,随机初始化位置    for j=1:D        if j==D% % 隐含层节点与训练次数是整数 学习率是浮点型            x(i,j)=(xmax(j)-xmin(j))*rand+xmin(j);        else            x(i,j)=round((xmax(j)-xmin(j))*rand+xmin(j));  %        end    end        v(i,:)=rand(1,D);end%------先计算各个粒子的适应度，并初始化Pi和Pg----------------------for i=1:N    p(i)=fitness(x(i,:),p_train,t_train,p_test,t_test);    y(i,:)=x(i,:);    end[fg,index]=min(p);pg = x(index,:);             %Pg为全局最优%------进入主要循环，按照公式依次迭代------------for t=1:M        for i=1:N        v(i,:)=w*v(i,:)+c1*rand*(y(i,:)-x(i,:))+c2*rand*(pg-x(i,:));        x(i,:)=x(i,:)+v(i,:);                        for j=1:D            if j ~=D                x(i,j)=round(x(i,j));            end            if x(i,j)>xmax(j) | x(i,j)<xmin(j)                if j==D                    x(i,j)=(xmax(j)-xmin(j))*rand+xmin(j);  %                else                    x(i,j)=round((xmax(j)-xmin(j))*rand+xmin(j));  %                end            end        end        temp=fitness(x(i,:),p_train,t_train,p_test,t_test);        if temp<p(i)            p(i)=temp;            y(i,:)=x(i,:);        end                if p(i)<fg            pg=y(i,:);            fg=p(i);        end    end    trace(t)=fg;    result(t,:)=pg;

参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/127596777?spm=1001.2014.3001.5501
[2] Https://download.csdn.net/download/kjm13182345320/86830096?spm=1001.2014.3001.5501

来源地址：https://blog.csdn.net/kjm13182345320/article/details/132557833