GWO-LSTM灰狼算法优化长短期记忆神经网络的数据分类预测（Matlab完整程序和数据），多输入单输出模型

% Grey Wolf Optimizer function [Alpha_score,Alpha_pos,Convergence_curve]=GWO(inputnum,outputnum,inputn,label_train,inputn_test,true_label) SearchAgents_no=20; % Number of search agents Max_iter=60; % Maximum numbef of iterations % Load details of the selected benchmark function ccc = 0; dim=2; ub = [60 0.02]; lb = [10 0.0001]; % ub = (1*ones(numsum,1))';%上边界,可以修改 % lb = (-1*ones(numsum,1))';%下边界,可以修改 % 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 [predict_label,y] = fun(Positions(i,:),inputnum,outputnum,inputn,label_train,inputn_test,true_label); fitness = 1-y; % Update Alpha, Beta, and Delta if fitness<Alpha_score vvv = predict_label; 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 a = 1-1*(1/(exp(1))*(exp(l/Max_iter))); % 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; end end l=l+1; Convergence_curve(l)=1-Alpha_score; disp(['GWO: At iteration ', num2str(l), ' ,the best fitness is ', num2str(Convergence_curve(l))]); end figure; stem(true_label,'*') hold on plot(vvv,'p') yticks(0:1:2) xlabel('样本','fontsize',12,'fontname','TimesNewRoman'); ylabel('标签值','fontsize',12,'fontname','TimesNewRoman'); title(['GWO-LSTM分类准确率：',num2str(Convergence_curve(l)*100),'%'],'fontsize',12,'fontname','TimesNewRoman'); legend('实际值','预测值','fontsize',12,'fontname','TimesNewRoman'); %% 画方框图 confMat = confusionmat(true_label,vvv); %output_test是真实值标签 figure; set(gca,'looseInset',[0 0 0 0]) % set(gcf,'unit','centimeters','position',[5 2 23 15]) plotConfMat(confMat.'); xlabel('预测标签','fontsize',12,'fontname','TimesNewRoman'); ylabel('真实标签','fontsize',12,'fontname','TimesNewRoman'); title(['GWO-LSTM分类准确率：',num2str(Convergence_curve(l)*100),'%'],'fontsize',12,'fontname','TimesNewRoman'); hold off end