时序预测 | MATLAB实现GWO-ELM灰狼优化算法优化极限学习机时间序列预测
目录
时序预测 | MATLAB实现GWO-ELM灰狼优化算法优化极限学习机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GWO-ELM灰狼优化算法优化极限学习机时间序列预测；
2.单变量时间序列预测；
3.运行环境Matlab及以上,运行主程序main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；
4.SSA-ELM麻雀算法优化极限学习机权值和偏置，命令窗口输出RMSE、MAE、R2、MAPE等评价指标。
程序设计
完整程序和数据下载方式1(资源处直接下载)：MATLAB实现GWO-ELM灰狼优化算法优化极限学习机时间序列预测完整程序和数据下载方式2(订阅《ELM极限学习机》专栏，同时可阅读《ELM极限学习机》专栏收录的所有内容，数据订阅后私信我获取)：MATLAB实现GWO-ELM灰狼优化算法优化极限学习机时间序列预测完整程序和数据下载方式3(订阅《智能学习》专栏，同时获取《智能学习》专栏收录程序5份，数据订阅后私信我获取)：MATLAB实现GWO-ELM灰狼优化算法优化极限学习机时间序列预测% Grey Wolf Optimizerfunction [Best_pos,Best_score,curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)% initialize alpha, beta, and delta_posBest_pos=zeros(1,dim);Best_score=inf; %change this to -inf for maximization problemsBeta_pos=zeros(1,dim);Beta_score=inf; %change this to -inf for maximization problemsDelta_pos=zeros(1,dim);Delta_score=inf; %change this to -inf for maximization problems%Initialize the positions of search agentsPositions=initialization(SearchAgents_no,dim,ub,lb);curve=zeros(1,Max_iter);l=0;% Loop counter% Main loopwhile l<Max_iterfor i=1:size(Positions,1) % Return back the search agents that go beyond the boundaries of the search spaceFlag4ub=Positions(i,:)>ub;Flag4lb=Positions(i,:)<lb;Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;% Calculate objective function for each search agentfitness=fobj(Positions(i,:));% Update Alpha, Beta, and Deltaif fitness<Best_score Best_score=fitness; % Update alphaBest_pos=Positions(i,:);endif fitness>Best_score && fitness<Beta_score Beta_score=fitness; % Update betaBeta_pos=Positions(i,:);endif fitness>Best_score && fitness>Beta_score && fitness<Delta_score Delta_score=fitness; % Update deltaDelta_pos=Positions(i,:);endenda=2-l*((2)/Max_iter); % a decreases linearly fron 2 to 0% Update the Position of search agents including omegasfor i=1:size(Positions,1)for j=1:size(Positions,2)r1=rand(); % r1 is a random number in [0,1]r2=rand(); % r2 is a random number in [0,1]A1=2*a*r1-a; % Equation (3.3)C1=2*r2; % Equation (3.4)D_alpha=abs(C1*Best_pos(j)-Positions(i,j)); % Equation (3.5)-part 1X1=Best_pos(j)-A1*D_alpha; % Equation (3.6)-part 1r1=rand();r2=rand();A2=2*a*r1-a; % Equation (3.3)C2=2*r2; % Equation (3.4)D_beta=abs(C2*Beta_pos(j)-Positions(i,j)); % Equation (3.5)-part 2X2=Beta_pos(j)-A2*D_beta; % Equation (3.6)-part 2 r1=rand();r2=rand(); A3=2*a*r1-a; % Equation (3.3)C3=2*r2; % Equation (3.4)D_delta=abs(C3*Delta_pos(j)-Positions(i,j)); % Equation (3.5)-part 3X3=Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3 Positions(i,j)=(X1+X2+X3)/3;% Equation (3.7)endendl=l+1; curve(l)=Best_score;end
参考资料
[1] /kjm13182345320/article/details/129215161
[2] /kjm13182345320/article/details/128105718
