【ElM分类】基于灰狼算法优化ElM神经网络实现数据分类附matlab代码

1 简介

为了提高核极限学习机(ELM)的分类正确率,采用灰狼算法(GWO)对惩罚系数,宽度参数两个参数进行优化.首先,根据乳腺良恶性肿瘤数据库训练集并利用灰狼算法优化核极限学习机;然后,通过GWO-ELM和ELM对测试集进行分类诊断;最后,对比分析GWO-ELM和ELM的分类性能,测试结果表明,GWO-ELM的总体诊断正确率相较于ELM提高了10%,且恶性肿瘤的诊断正确率明显优于ELM.​

2 部分代码

​​% Grey Wolf Optimizerfunction [Alpha_score,Alpha_pos,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fhandle,fnonlin)​% initialize alpha, beta, and delta_posAlpha_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 agentsPositions=initialization(SearchAgents_no,ub,lb);​Convergence_curve=zeros(1,Max_iter);​l=0;% Loop counter​% Main loopwhile 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=Fun(fhandle,fnonlin,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

4 参考文献

[1]赵国栋, 高旭, 张烜,等. 一种基于GWO-OSELM的非接触式手掌活体检测方法及装置:, CN112257688A[P]. 2021.

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