Transformer-SVM组合模型多特征分类预测/故障诊断（Matlab实现）小样本故障诊断新思路

在Matlab中实现组合Transformer-SVM模型进行多特征分类预测和故障诊断可以为小样本故障诊断问题带来新的思路。以下是一个简化的示例代码框架，结合Transformer和SVM模型进行故障诊断的实现：

ini

复制
% 加载和准备数据（示例）
data = … % 加载特征数据
labels = … % 加载标签数据

% 数据预处理
% 这里假设数据已经进行了特征提取和预处理

% 划分训练集和测试集
cv = cvpartition(labels, ‘HoldOut’, 0.3);
trainIdx = training(cv); % 训练集索引
testIdx = test(cv); % 测试集索引

X_train = data(trainIdx, 😃;
Y_train = labels(trainIdx);

X_test = data(testIdx, 😃;
Y_test = labels(testIdx);

% Transformer模型
transformer_layers = [
fullyConnectedLayer()
reluLayer
fullyConnectedLayer(32)
reluLayer
fullyConnectedLayer(16)
];

% 训练Transformer模型
transformer_model = [
sequenceInputLayer(size(X_train, 2))
transformer_layers
fullyConnectedLayer(numel(unique(labels)))
softmaxLayer
classificationLayer
];

options = trainingOptions(‘adam’, …
‘MaxEpochs’, 50, …
‘MiniBatchSize’, , …
‘Plots’, ‘training-progress’);

transformer_net = trainNetwork(X_train, categorical(Y_train), transformer_model, options);

% 提取Transformer模型特征
transformer_features_train = activations(transformer_net, X_train, ‘transformer_layers’, ‘MiniBatchSize’, , ‘OutputAs’, ‘rows’);
transformer_features_test = activations(transformer_net, X_test, ‘transformer_layers’, ‘MiniBatchSize’, , ‘OutputAs’, ‘rows’);

% SVM模型
svm_model = fitcecoc(transformer_features_train, Y_train);

% 预测
Y_pred = predict(svm_model, transformer_features_test);

% 评估模型性能
accuracy = sum(Y_pred == Y_test) / numel(Y_test);
disp([‘准确率：’, num2str(accuracy)]);