基于深度学习的超声波探伤系统设计

Question

基于深度学习的超声波探伤系统设计MATLAB代码实现，对获得的数据进行降噪，特征提取，BP神经网络进行训练，判断内部损伤。

Answer 1

该回答引用ChatGPT GPT-4

运行结果

代码如下

clear
% 加载超声波数据
load('ultrasonic_data.mat'); % 替换为您的数据文件

% 使用小波进行降噪处理
num_samples = size(signal, 1);
denoisedSignal = zeros(size(signal));
for i = 1:num_samples
    denoisedSignal(i, :) = wdenoise(signal(i, :), 5, 'Wavelet', 'db4', 'DenoisingMethod', 'UniversalThreshold');
end

% 使用傅里叶变换进行特征提取
num_features = 100;
features = abs(fft(denoisedSignal, [], 2));
features = features(:, 1:num_features);

% 归一化特征
normalizedFeatures = (features - mean(features, 1)) ./ std(features, 1);

% 准备训练和测试数据
trainRatio = 0.8;
[trainInd, ~, testInd] = dividerand(size(normalizedFeatures, 1), trainRatio, 0, 1 - trainRatio);
trainData = normalizedFeatures(trainInd, :);
trainLabels = labels(trainInd);
testData = normalizedFeatures(testInd, :);
testLabels = labels(testInd);

% 创建并训练BP神经网络
hiddenLayerSize = 20;
net = patternnet(hiddenLayerSize);
net.divideParam.trainRatio = 1;
net.divideParam.valRatio = 0;
net.divideParam.testRatio = 0;
net.trainParam.epochs = 200;
net.trainParam.goal = 1e-5;
net.trainParam.min_grad = 1e-5;
trainTargets = ind2vec(trainLabels');
[net, tr] = train(net, trainData', trainTargets);

% 测试网络
predictedLabels = net(testData');
[~, predictedClass] = max(predictedLabels);
actualClass = testLabels;
accuracy = sum(predictedClass' == actualClass) / length(actualClass);
fprintf('Accuracy: %.2f%%\n', accuracy * 100);