CNN-LSTM寿命预测模型MATLAB相关代码

以下是一个简单的CNN-LSTM寿命预测模型MATLAB代码示例：

%% 数据准备
load('data.mat'); % 导入数据
X = data(:,1:end-1); % 特征变量
Y = data(:,end); % 目标变量

%% 数据预处理
% 标准化特征变量
mu = mean(X);
sigma = std(X);
X = (X - mu) ./ sigma;

% 将数据划分为训练集和测试集
[trainInd,testInd] = dividerand(size(X,1),0.7,0.3); % 70%训练，30%测试

% 将数据转换为时间序列格式
XTrain = cell(size(trainInd));
YTrain = cell(size(trainInd));
for i = 1:length(trainInd)
    XTrain{i} = transpose(X(trainInd(i)-99:trainInd(i),:));
    YTrain{i} = Y(trainInd(i));
end

XTest = cell(size(testInd));
YTest = cell(size(testInd));
for i = 1:length(testInd)
    XTest{i} = transpose(X(testInd(i)-99:testInd(i),:));
    YTest{i} = Y(testInd(i));
end

%% 构建模型
% CNN层
inputSize = size(XTrain{1},1);
filterSize = 5;
numFilters = 16;
poolSize = 2;
convLayer = sequenceInputLayer(inputSize,'Name','input');
convLayer = addConvolution2dLayer(convLayer,numFilters,filterSize,'Name','conv');
convLayer = addReLUActivationLayer(convLayer,'Name','relu');
convLayer = addMaxPooling2dLayer(convLayer,poolSize,'Stride',2,'Name','maxpool');

% LSTM层
numHiddenUnits = 100;
lstmLayer = addLSTMLayer(convLayer,numHiddenUnits,'OutputMode','last','Name','lstm');

% 全连接层
numClasses = 1;
fcLayer = addFullyConnectedLayer(lstmLayer,numClasses,'Name','fc');

% 输出层
outputLayer = regressionLayer('Name','output');

% 将层组装成网络
layers = [convLayer;lstmLayer;fcLayer;outputLayer];
net = assembleNetwork(layers);

%% 训练模型
options = trainingOptions('adam', ...
    'MaxEpochs',50, ...
    'MiniBatchSize',64, ...
    'ValidationData',{XTest,YTest}, ...
    'ValidationFrequency',10, ...
    'Plots','training-progress');

net = trainNetwork(XTrain,YTrain,net,options);

%% 预测和评估
YPred = predict(net,XTest);
rmse = sqrt(mean((YTest - YPred).^2));
fprintf('RMSE: %f\n',rmse);

请注意，此示例代码仅用于展示CNN-LSTM寿命预测模型的基本结构和MATLAB中的实现方式。实际应用中，您需要根据数据的特点进行适当的调整和优化，以获得更好的预测结果。