【ELM预测】基于灰狼算法优化极限学习机预测附matlab代码

1 简介

准确的电池荷电状态(SOC)估计是电动车辆正常工作的基本前提.针对目前电池荷电状态估计时存在的非线性,不平稳等干扰因素的影响,本工作提出了基于灰狼优化算法的极限学习机的锂离子电池SOC估计方法,以提高估计精度并缩短估计时长.传统的极限学习机(ELM)直接随机生成模型参数,并对SOC进行估计,该方法运行速度快且泛化性能好.但极限学习机需要找出最优的隐含层神经元参数才能达到较高的精度.因此,通过灰狼优化算法(GWO)进一步优化模型参数,并通过选择合适的激活函数,弥补了传统极限学习机的不足.

2 部分代码

%___________________________________________________________________%
                                                  %
%___________________________________________________________________%
% Grey Wolf Optimizer
function [Alpha_score,Alpha_pos,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)
% initialize alpha, beta, and delta_pos
Alpha_pos=zeros(1,dim);
Alpha_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);
Convergence_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<Alpha_score 
            Alpha_score=fitness; % Update alpha
            Alpha_pos=Positions(i,:);
        end
        if fitness>Alpha_score && fitness<Beta_score 
            Beta_score=fitness; % Update beta
            Beta_pos=Positions(i,:);
        end
        if fitness>Alpha_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*Alpha_pos(j)-Positions(i,j)); % Equation (3.5)-part 1
            X1=Alpha_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;    
    Convergence_curve(l)=Alpha_score;
end      

3 仿真结果

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编辑

4 参考文献

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