基于双向长短时记忆神经网络结合多头注意力机制BiLSTM-Mutilhead-Attention实现柴油机故障诊断附matlab代码

% 加载数据集和标签
load(‘diesel_dataset.mat’); % 假设数据集存储在 diesel_dataset.mat 文件中
data = diesel_dataset.data;
labels = diesel_dataset.labels;

% 数据预处理
% 这里假设你已经完成了数据的预处理，包括特征提取、归一化等步骤

% 划分训练集和测试集
[trainData, trainLabels, testData, testLabels] = splitData(data, labels, 0.8);

% 定义模型参数
inputSize = size(trainData, 2);
numClasses = numel(unique(labels));
hiddenSize = 128;
numLayers = 2;
numHeads = 4;

% 构建双向LSTM层
bilstmLayer = bidirectionalLSTMLayer(hiddenSize, “OutputMode”, “sequence”);

% 构建多头注意力层
attentionLayer = multiheadAttentionLayer(hiddenSize, numHeads);

% 构建分类层
classificationLayer = classificationLayer(“Name”, “classification”);

% 构建网络模型
layers = [
sequenceInputLayer(inputSize, “Name”, “input”)
bilstmLayer
attentionLayer
classificationLayer
];

% 定义训练选项
options = trainingOptions(“adam”, …
“MaxEpochs”, 20, …
“MiniBatchSize”, 32, …
“Plots”, “training-progress”);

% 训练模型
net = trainNetwork(trainData, categorical(trainLabels), layers, options);

% 在测试集上评估模型
predictions = classify(net, testData);
accuracy = sum(predictions == categorical(testLabels)) / numel(testLabels);
disp(“测试集准确率: ” + accuracy);

% 辅助函数：划分数据集
function [trainData, trainLabels, testData, testLabels] = splitData(data, labels, trainRatio)
numSamples = size(data, 1);
indices = randperm(numSamples);
trainSize = round(trainRatio * numSamples);
trainIndices = indices(1:trainSize);
testIndices = indices(trainSize+1:end);

trainData = data(trainIndices, :);
trainLabels = labels(trainIndices);
testData = data(testIndices, :);
testLabels = labels(testIndices);

end