回归预测 | MATLAB实现GWO-ELM灰狼算法优化极限学习机多输入单输出

回归预测 | MATLAB实现GWO-ELM灰狼算法优化极限学习机多输入单输出

效果一览

基本介绍

MATLAB实现GWO-ELM灰狼算法优化极限学习机多输入单输出
优化参数为权值和阈值，自带数据为excel数据，多输入，单输出，回归预测。
灰狼优化算法（GWO）模拟了自然界灰狼的领导和狩猎层级，在狼群中存在四种角色，狼1负责领导是最具有智慧的在狩猎当中可以敏锐的知道猎物的位置，狼2可以认为是军师比较具有智慧比较能知道猎物的位置，狼3负责协助前两个层级的狼，最后是狼4负责跟从。

程序设计

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% Grey Wolf Optimizer
function [Best_pos,Best_score,curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)

% initialize alpha, beta, and delta_pos
Best_pos=zeros(1,dim);
Best_score=inf; %change this to -inf for maximization problems

Beta_pos=zeros(1,dim);
Beta_score=inf; %change this to -inf for maximization problems

Delta_pos=zeros(1,dim);
Delta_score=inf; %change this to -inf for maximization problems

%Initialize the positions of search agents
Positions=initialization(SearchAgents_no,dim,ub,lb);

curve=zeros(1,Max_iter);

l=0;% Loop counter

% Main loop
while l<Max_iter
    for i=1:size(Positions,1)  
        
       % Return back the search agents that go beyond the boundaries of the search space
        Flag4ub=Positions(i,:)>ub;
        Flag4lb=Positions(i,:)<lb;
        Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;               
        
        % Calculate objective function for each search agent
        fitness=fobj(Positions(i,:));
        
        % Update Alpha, Beta, and Delta
        if fitness<Best_score 
            Best_score=fitness; % Update alpha
            Best_pos=Positions(i,:);
        end
        
        if fitness>Best_score && fitness<Beta_score 
            Beta_score=fitness; % Update beta
            Beta_pos=Positions(i,:);
        end
        
        if fitness>Best_score && fitness>Beta_score && fitness<Delta_score 
            Delta_score=fitness; % Update delta
            Delta_pos=Positions(i,:);
        end
    end
       
    a=2-l*((2)/Max_iter); % a decreases linearly fron 2 to 0
    
    % Update the Position of search agents including omegas
    for 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 1
            X1=Best_pos(j)-A1*D_alpha; % Equation (3.6)-part 1
                       
            r1=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 2
            X2=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 3
            X3=Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3             
            
            Positions(i,j)=(X1+X2+X3)/3;% Equation (3.7)
            
        end
    end
    l=l+1;    
    curve(l)=Best_score;
end

学习总结

极限学习机，为人工智能机器学习领域中的一种人工神神经网络模型，是一种求解单隐层前馈神经网路的学习演算法。极限学习机是用于分类、回归、聚类、稀疏逼近、压缩和特征学习的前馈神经网络，具有单层或多层隐层节点，其中隐层节点的参数（不仅仅是将输入连接到隐层节点的权重）不需要被调整。这些隐层节点可以随机分配并且不必再更新（即它们是随机投影但具有非线性变换），或者可以从其祖先继承下来而不被更改。在大多数情况下，隐层节点的输出权重通常是一步学习的，这本质上相当于学习一个线性模型。

参考资料

[1] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: A new learning scheme of feedforward neural networks,” in Proc. Int. Joint Conf. Neural Networks, July 2004, vol. 2, pp. 985–990.
[2] https://blog.csdn.net/kjm13182345320/article/details/127361354