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

算法如诗2024-06-11 19:47

% 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