帮我写“基于 LSTM 的短期风速软测量”的matlab代码，使用data.xlsx数据，共计2列数据，全部数据为65535行，第1列数据为原始风速、第2列数据为分钟风速变化。要分出训练数据和测试数据，并给出风速预测结果和实际结果的对比曲线，要出现预测结果的曲线、训练误差曲线和相对误差曲线。

好的，以下是一个基于LSTM的短期风速软测量的MATLAB代码示例，使用data.xlsx数据，共计2列数据，全部数据为65535行，第1列数据为原始风速、第2列数据为分钟风速变化。代码实现了数据预处理、模型构建、训练和预测等步骤，并绘制了预测结果的曲线、训练误差曲线和相对误差曲线。

% Load data from data.xlsx
data = readtable('data.xlsx');

% Normalize data
data = table2array(data);
data_norm = (data - min(data)) / (max(data) - min(data));

% Split data into training and testing sets
train_ratio = 0.8;
train_size = round(train_ratio * size(data_norm, 1));
train_data = data_norm(1:train_size, :);
test_data = data_norm(train_size+1:end, :);

% Create LSTM network
num_features = 2; % number of features in the input data
num_hidden_units = 32; % number of hidden units in the LSTM layer
num_layers = 2; % number of LSTM layers
output_size = 1; % number of output values
net = lstm(num_hidden_units, num_layers, 'OutputMode', 'last');

% Train LSTM network
max_epochs = 100; % maximum number of training epochs
mini_batch_size = 64; % mini-batch size for training
XTrain = train_data(:, 1); % input data for training
YTrain = train_data(:, 2); % target data for training
training_options = trainingOptions('adam', ...
    'MaxEpochs', max_epochs, ...
    'MiniBatchSize', mini_batch_size, ...
    'GradientThreshold', 1, ...
    'InitialLearnRate', 0.01, ...
    'LearnRateSchedule', 'piecewise', ...
    'LearnRateDropFactor', 0.1, ...
    'LearnRateDropPeriod', 50, ...
    'Verbose', 1, ...
    'Plots', 'training-progress');
[net, training_info] = trainNetwork(XTrain, YTrain, net, training_options);

% Predict wind speed for testing data
XTest = test_data(:, 1); % input data for testing
YTest = test_data(:, 2); % target data for testing
YPred = predict(net, XTest);

% Denormalize data
XTest = XTest * (max(data) - min(data)) + min(data);
YTest = YTest * (max(data) - min(data)) + min(data);
YPred = YPred * (max(data) - min(data)) + min(data);

% Plot prediction results
figure
plot(YTest, 'b')
hold on
plot(YPred, 'r')
xlabel('Time (minutes)')
ylabel('Wind speed')
legend('Actual', 'Predicted')

% Plot training error curve
figure
plot(training_info.TrainingLoss)
xlabel('Epoch')
ylabel('Training loss')

% Calculate relative error
relative_error = abs(YPred - YTest) ./ YTest;

% Plot relative error curve
figure
plot(relative_error)
xlabel('Time (minutes)')
ylabel('Relative error')

希望这个代码示例对您有所帮助。请注意，这只是一个简单的示例，您可能需要根据您的具体数据和问题进行修改和调整。