BiLSTM双向长短期记忆神经网络故障诊断/分类预测（Matlab完整源码）

%% 初始化 clear close all clc warning off % 数据读取 addpath(genpath(pwd)); load ('data.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); output =data(:,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_test,inputtestps]=mapminmax('apply',input_test,inputps); output_train = categorical(output_train); output_test = categorical(output_test); for i = 1:size(input_train,2) Train_xNorm{i,:} = reshape(inputn_train(:,i),hhh,1,1); Train_yNorm{i,:} = output_train(i); end for i = 1:size(input_test,2) Test_xNorm{i,:} = reshape(inputn_test(:,i),hhh,1,1); Test_yNorm{i,:} = output_test(i); end numFeatures = size(input,2); numHiddens = 120; numClasses = 10; % 创建BiLSTM网络， layers = [ ... sequenceInputLayer(numFeatures) % 输入层 bilstmLayer(32) reluLayer fullyConnectedLayer(numClasses,'name','fullconnect') % 全连接层设置（影响输出维度）（cell层出来的输出层） % softmaxLayer('Name','softmax') classificationLayer('name','output')]; % 参数设置 options = trainingOptions('adam', ... % 优化算法Adam 'MaxEpochs', 150, ... % 最大训练次数 'GradientThreshold', 1, ... % 梯度阈值 'InitialLearnRate', 0.01, ... % 初始学习率 'LearnRateSchedule', 'piecewise', ... % 学习率调整 'LearnRateDropPeriod', 100, ... % 训练850次后开始调整学习率 'LearnRateDropFactor',0.1, ... % 学习率调整因子 'L2Regularization', 0.0001, ... % 正则化参数 'ExecutionEnvironment', 'cpu',... % 训练环境 'Verbose', 1, ... % 关闭优化过程 'Plots', 'none'); % 画出曲线 % 训练 tic net = trainNetwork(Train_xNorm, Train_yNorm, layers, options); toc %analyzeNetwork(net);% 查看网络结构 % 预测 pred1 = classify(net, Test_xNorm); pred = categorical(); for i = 1:size(pred1,1) pred(i,1) = string(pred1{i,:}); end accuracy=sum(output_test==pred)/length(pred); %计算预测的确率 % 标准bilstm作图 % 画方框图 figure('Position',[10,50,800,600]) set(gca,'looseInset',[0 0 0 0]) confMat = confusionmat(output_test,pred); %output_test是真实值标签 zjyanseplotConfMat(confMat.'); xlabel('Predicted label') ylabel('Real label') title(['Bilstm的测试集正确率 = ',num2str(accuracy*100),' %']) % 作图 figure('Position',[50,50,800,600]) set(gca,'looseInset',[0 0 0 0]) plot(1:1:length(pred),output_test,'*-','Color',[0 0.4470 0.7410],'LineWidth',1) hold on plot(1:1:length(pred),pred,'p-','Color',[0.9290 0.6940 0.1250],'LineWidth',0.1,'MarkerSize',3) legend('预测类别','真实类别','NorthWest') title(['BiLSTM的测试集正确率 = ',num2str(accuracy*100),' %']) xlabel('预测样本编号') ylabel('分类结果') box on set(gca,'fontsize',12)