% 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