给我一断多信号分类法求随机信号功率谱的matlab代码

一个常用的多信号分类方法是基于卷积神经网络（Convolutional Neural Network, CNN），下面是一个简单的基于CNN的随机信号功率谱分类MATLAB代码示例：

% 生成随机信号和标签
num_samples = 1000;
num_classes = 4;

% 每个样本有1000个数据点
signal_length = 1000;

% 生成随机信号
signals = rand(num_samples, signal_length);

% 计算功率谱密度
psd = abs(fft(signals, [], 2)).^2 / signal_length;

% 将功率谱密度转换为图像（类似于图像分类的输入）
psd_images = reshape(psd, [num_samples, signal_length, 1]);

% 生成随机标签
labels = randi(num_classes, [num_samples, 1]);

% 将标签转换为独热编码（one-hot encoding）
labels_onehot = ind2vec(labels');

% 将数据集分为训练集和测试集
train_ratio = 0.7;
[train_psd_images, train_labels_onehot, test_psd_images, test_labels_onehot] = split_dataset(psd_images, labels_onehot, train_ratio);

% 构建CNN模型
num_filters = 16;
filter_size = 32;
pool_size = 4;
num_hidden_units = 32;

layers = [
    imageInputLayer([signal_length, 1, 1])
    
    convolution2dLayer([filter_size, 1], num_filters)
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer([pool_size, 1])
    
    convolution2dLayer([filter_size, 1], num_filters)
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer([pool_size, 1])
    
    fullyConnectedLayer(num_hidden_units)
    reluLayer
    
    dropoutLayer(0.5)
    
    fullyConnectedLayer(num_classes)
    softmaxLayer
    classificationLayer
];

% 设置训练选项
options = trainingOptions('adam', ...
    'MaxEpochs', 20, ...
    'MiniBatchSize', 64, ...
    'ValidationData', {test_psd_images, test_labels_onehot}, ...
    'ValidationFrequency', 10, ...
    'Verbose', false);

% 训练模型
trained_net = trainNetwork(train_psd_images, train_labels_onehot, layers, options);

% 测试模型
test_pred = classify(trained_net, test_psd_images);
test_acc = sum(test_pred == find(test_labels_onehot)) / numel(test_labels_onehot);
fprintf('Test accuracy: %.2f%%\n', test_acc * 100);

注意：这只是一个简单的示例代码，实际使用时需要根据具体场景进行调整。