HBA-KELM的蜜獾算法优化核极限学习机故障诊断，优化前后对比（Matlab完整源码和数据）

%% 初始化 clear close all clc warning off %% 数据读取 addpath(genpath(pwd)); load vmddata.mat %加载处理好的特征数据 data = vmddata; %% 数据载入 bv = 120; %每种状态数据有120组 % 加标签值 hhh = size(data,2); for i=1:size(data,1)/bv data(1+bv*(i-1):bv*i,hhh+1)=i; end input=data(:,1:hhh); output1 =data(:,end); for i=1:size(data,1) %一共10种类型 switch output1(i) case 1 output(i,1)=1; case 2 output(i,2)=1; case 3 output(i,3)=1; case 4 output(i,4)=1; case 5 output(i,5)=1; case 6 output(i,6)=1; case 7 output(i,7)=1; case 8 output(i,8)=1; case 9 output(i,9)=1; case 10 output(i,10)=1; end end jg = bv; %每组120个样本 tn = 90; %选前tn个样本进行训练 input_train = []; output_train = []; input_test = []; output_test = []; for i = 1:max(data(:,end)) input_train=[input_train;input(1+jg*(i-1):jg*(i-1)+tn,:)]; output_train=[output_train;output(1+jg*(i-1):jg*(i-1)+tn,:)]; input_test=[input_test;input(jg*(i-1)+tn+1:i*jg,:)]; output_test=[output_test;output(jg*(i-1)+tn+1:i*jg,:)]; end input_train = input_train'; input_test = input_test'; %归一化 [inputn_train,inputps]=mapminmax(input_train);inputn_train = inputn_train'; [inputn_test,inputtestps]=mapminmax('apply',input_test,inputps); inputn_test =inputn_test'; pop=[1 1]; kernel='RBF_kernel';%核函数 % 训练过程 n_sample=size(inputn_train,1);%样本数 Omega_train = kernel_matrix(inputn_train,kernel, pop(1));%隐含层输出 OutputWeight=((Omega_train+speye(n_sample)/pop(2))\(output_train));%输出权重 Y1=Omega_train * OutputWeight;%训练集预测输出 % 测试过程 Omega_test = kernel_matrix(inputn_train,kernel, pop(1),inputn_test)'; Yt_hat=Omega_test * OutputWeight;%测试集预测输出 [~,J1]=max(Yt_hat,[],2);%将预测值与真实值的one-hot编码转换为类别标签 [~,J]=max(output_test,[],2); test_accuracy =sum(J==J1)/length(J); figure; stem(J); hold on; plot(J1, 'r*'); legend('真实标签', '预测标签'); title(['没有优化的KELM分类精度为：',num2str(test_accuracy*100),'%']) hold off; %画方框图 confMat = confusionmat(J1,J); %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]) zjyanseplotConfMat(confMat.'); xlabel('预测标签','fontsize',12,'fontname','TimesNewRoman'); ylabel('真实标签','fontsize',12,'fontname','TimesNewRoman'); title(['没有优化的KELM分类精度为：',num2str(test_accuracy*100),'%'],'fontsize',12,'fontname','TimesNewRoman'); hold off %% 优化KELM %% kernel='RBF_kernel'; SearchAgents_no = 10; %种群个数 Max_iter = 20; % 迭代次数 lb = 0; %下限 ub = 1000; %上限 dim = 2; %维度 fobj = @(x)fun(x,inputn_train,output_train,inputn_test,output_test,kernel); disp('正在调用优化算法对KELM进行优化，请稍等……') [Best_score,Best_pos,curve]=HBA(SearchAgents_no, Max_iter,lb,ub,dim,fobj); figure plot(curve,'-o','LineWidth',1) xlabel('迭代次数') ylabel('适应度值') title('优化KELM的适应度曲线') setdemorandstream(2*pi); %将最佳值Best_pos带入目标函数 [error,J2] = fun(Best_pos,inputn_train,output_train,inputn_test,output_test,kernel); test_accuracy = 1-error; figure; stem(J); hold on; plot(J2, 'r*'); legend('真实标签', '预测标签'); title(['优化后KELM分类精度为：',num2str((test_accuracy)*100),'%']) hold off; %画方框图 confMat = confusionmat(J,J2); %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]) zjyanseplotConfMat(confMat.'); xlabel('预测标签','fontsize',12,'fontname','TimesNewRoman'); ylabel('真实标签','fontsize',12,'fontname','TimesNewRoman'); title(['优化后KELM分类精度为：',num2str(test_accuracy*100),'%'],'fontsize',12,'fontname','TimesNewRoman'); hold off