基于lstm模型写一段预测天气的matlab代码
时间: 2024-01-29 14:34:44 浏览: 136
下面是一个简单的基于LSTM模型的天气预测Matlab代码示例:
```matlab
% 导入数据
data = readtable('weather_data.csv');
data = table2array(data(:, 2:end));
% 特征归一化
min_data = min(data);
max_data = max(data);
data = (data - min_data) ./ (max_data - min_data);
% 构造训练集和测试集
train_size = floor(size(data, 1) * 0.8);
train_data = data(1:train_size, :);
test_data = data(train_size+1:end, :);
% 构造输入和输出序列
input_seq_len = 30;
output_seq_len = 1;
train_X = [];
train_Y = [];
for i = input_seq_len+1:size(train_data, 1)-output_seq_len+1
train_X = [train_X; train_data(i-input_seq_len:i-1, :)];
train_Y = [train_Y; train_data(i+output_seq_len-1, :)];
end
test_X = [];
test_Y = [];
for i = input_seq_len+1:size(test_data, 1)-output_seq_len+1
test_X = [test_X; test_data(i-input_seq_len:i-1, :)];
test_Y = [test_Y; test_data(i+output_seq_len-1, :)];
end
% 构造LSTM模型
num_features = size(train_X, 2);
num_hidden = 32;
num_layers = 2;
num_classes = size(train_Y, 2);
net = lstmLayer(num_hidden, 'OutputMode', 'last', 'InputWeightsInitializer', 'glorot', 'RecurrentWeightsInitializer', 'orthogonal');
net = addLayers(net, lstmLayer(num_hidden, 'OutputMode', 'last', 'InputWeightsInitializer', 'glorot', 'RecurrentWeightsInitializer', 'orthogonal'));
net = addLayers(net, fullyConnectedLayer(num_classes));
net = addLayers(net, regressionLayer());
net = connectLayers(net, 'output', 'RegressionLayer_Target');
% 训练LSTM模型
max_epoch = 100;
mini_batch_size = 32;
options = trainingOptions('adam', 'MaxEpochs', max_epoch, 'MiniBatchSize', mini_batch_size, 'GradientThreshold', 1, 'InitialLearnRate', 0.01);
[net, info] = trainNetwork(train_X', train_Y', net, options);
% 预测测试集结果
test_Y_pred = predict(net, test_X')';
% 反归一化
test_Y = test_Y .* (max_data - min_data) + min_data;
test_Y_pred = test_Y_pred .* (max_data - min_data) + min_data;
% 显示预测结果
plot(test_Y);
hold on;
plot(test_Y_pred);
legend('真实值', '预测值');
```
这个程序首先导入数据并进行特征归一化,然后将数据集分为训练集和测试集,并构造输入和输出序列。接着,构造了一个包含两层LSTM的模型,并使用Adam优化器进行训练。最后,使用训练好的模型对测试集进行预测,并将预测结果反归一化后绘制在图像上。
请注意,这只是一个简单的示例,实际应用中需要根据具体问题进行修改和优化。
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