Genetic Algorithm with PHM.rar_PHM_phm故障_phm程序_故障诊断 matlab_机械遗传算

%% 遗传算法的优化计算 %% 清空环境变量 clear all clc warning off %% 声明全局变量 global P_train T_train P_test T_test mint maxt S s1 S = 19; s1 = round(sqrt(19+1)+1); %% 导入数据 load data3.mat % 训练数据 P_train = input'; T_train = output; % 测试数据 P_test = test'; T_test = test_output; % 显示实验条件 count_1 = length(find(output == 1)); count_2 = length(find(output == 2)); count_3 = length(find(output == 3)); count_4 = length(find(output == 4)); number_1 = length(find(test_output == 1)); number_2 = length(find(test_output == 2)); number_3 = length(find(test_output == 3)); number_4 = length(find(test_output == 4)); disp('实验条件为：'); disp( ['训练集样本总数：' num2str(16)]); disp( ['活塞敲缸响：' num2str(count_1)]); disp( ['活塞销子响：' num2str(count_2)]); disp( ['连杆轴承响：' num2str(count_3)]); disp( ['曲轴轴承响：' num2str(count_4)]); disp( ['测试集样本总数：' num2str(8)]); disp( ['活塞敲缸响：' num2str(number_1)]); disp( ['活塞销子响：' num2str(number_2)]); disp( ['连杆轴承响：' num2str(number_3)]); disp( ['曲轴轴承响：' num2str(number_4)]); %% 创建单BP网络 t = cputime; net_bp = newff(minmax(P_train),[s1,1],{'tansig','purelin'},'trainlm'); % 设置训练参数 net_bp.trainParam.epochs = 1000; net_bp.trainParam.show = 10; net_bp.trainParam.goal = 1e-10; net_bp.trainParam.lr = 0.01; net_bp.trainParam.showwindow = 0; %% 训练单BP网络 net_bp = train(net_bp,P_train,T_train); %% 仿真测试单BP网络 T_bp_sim = sim(net_bp,P_test); e = cputime - t; T_bp_sim(T_bp_sim >= 3.5) = 4; T_bp_sim(T_bp_sim >= 2.5&T_bp_sim < 3.5) = 3; T_bp_sim(T_bp_sim >= 1.5&T_bp_sim < 2.5) = 2; T_bp_sim(T_bp_sim < 1.5) = 1; result_bp = [T_bp_sim' T_test']; %% 结果显示（单BP网络） number_1_sim = length(find(T_bp_sim == 1 & T_test == 1)); number_2_sim = length(find(T_bp_sim == 2 & T_test == 2)); number_3_sim = length(find(T_bp_sim == 3 & T_test == 3)); number_4_sim = length(find(T_bp_sim == 4 & T_test == 4)); % number_5_sim = length(find(T_bp_sim == 5 & T_test == 5)); disp('(1)BP网络的测试结果为：'); disp(['活塞敲缸响：' num2str(number_1_sim)... ' 误诊：' num2str(number_1 - number_1_sim)... ' 确诊率p1 = ' num2str(number_1_sim/number_1*100) '%']); disp(['活塞销子响：' num2str(number_2_sim)... ' 误诊：' num2str(number_2 - number_2_sim)... ' 确诊率p2 = ' num2str(number_2_sim/number_2*100) '%']); disp(['连杆轴承响：' num2str(number_3_sim)... ' 误诊：' num2str(number_3 - number_3_sim)... ' 确诊率p3 = ' num2str(number_3_sim/number_3*100) '%']); disp(['曲轴轴承响：' num2str(number_4_sim)... ' 误诊：' num2str(number_4 - number_4_sim)... ' 确诊率p4 = ' num2str(number_4_sim/number_4*100) '%']); disp(['建模时间为：' num2str(e) 's'] ); %% 遗传算法优化 popu = 20; bounds = ones(S,1)*[0,1]; % 产生初始种群 initPop = randi(popu,S,[0 1]); % 计算初始种群适应度 initFit = zeros(popu,1); for i = 1:size(initPop,1) initFit(i) = de_code(initPop(i,:)); end initPop = [initPop initFit]; gen = 100; % 优化计算 [X,EndPop,BPop,Trace] = ga(bounds,'fitness',[],initPop,[1e-6 1 0],'maxGenTerm',... gen,'normGeomSelect',0.09,'simpleXover',2,'boundaryMutation',[2 gen 3]); [m,n] = find(X == 1); disp(['优化筛选后的输入自变量编号为:' num2str(n)]); % 绘制适应度函数进化曲线 figure plot(Trace(:,1),Trace(:,3),'r:') hold on plot(Trace(:,1),Trace(:,2),'b') xlabel('进化代数') ylabel('适应度函数') title('适应度函数进化曲线') legend('平均适应度函数','最佳适应度函数') xlim([1 gen]) %% 新训练集/测试集数据提取 p_train = zeros(size(n,2),size(T_train,2)); p_test = zeros(size(n,2),size(T_test,2)); for i = 1:length(n) p_train(i,:) = P_train(n(i),:); p_test(i,:) = P_test(n(i),:); end t_train = T_train; %% 创建优化BP网络 t = cputime; net_ga = newff(minmax(p_train),[round(sqrt(numel(n)+1)+1),1],{'tansig','purelin'},'trainlm'); % 训练参数设置 net_ga.trainParam.epochs = 1000; net_ga.trainParam.show = 10; net_ga.trainParam.goal = 1e-10; net_ga.trainParam.lr = 0.01; net_ga.trainParam.showwindow = 0; %% 训练优化BP网络 net_ga = train(net_ga,p_train,t_train); %% 仿真测试优化BP网络 T_ga_sim = sim(net_ga,p_test); e = cputime - t; T_ga_sim(T_ga_sim >= 3.5) = 4; T_ga_sim(T_ga_sim >= 2.5&T_ga_sim < 3.5) = 3; T_ga_sim(T_ga_sim >= 1.5&T_ga_sim < 2.5) = 2; T_ga_sim(T_ga_sim < 1.5) = 1; result_ga = [T_ga_sim' T_test']; %% 结果显示（优化BP网络） number_11_sim = length(find(T_ga_sim == 1 & T_test == 1)); number_22_sim = length(find(T_ga_sim == 2 & T_test == 2)); number_33_sim = length(find(T_ga_sim == 3 & T_test == 3)); number_44_sim = length(find(T_ga_sim == 4 & T_test == 4)); disp('(2)优化BP网络的测试结果为：'); disp(['活塞敲缸响：' num2str(number_11_sim)... ' 误诊：' num2str(number_1 - number_11_sim)... ' 确诊率p1 = ' num2str(number_11_sim/number_1*100) '%']); disp(['活塞销子响：' num2str(number_22_sim)... ' 误诊：' num2str(number_2 - number_22_sim)... ' 确诊率p2 = ' num2str(number_22_sim/number_2*100) '%']); disp(['连杆轴承响：' num2str(number_33_sim)... ' 误诊：' num2str(number_3 - number_33_sim)... ' 确诊率p3 = ' num2str(number_33_sim/number_3*100) '%']); disp(['曲轴轴承响：' num2str(number_44_sim)... ' 误诊：' num2str(number_4 - number_44_sim)... ' 确诊率p4 = ' num2str(number_44_sim/number_4*100) '%']); disp(['建模时间为：' num2str(e) 's'] );