帮我写一个基于LSTM的七输入双输出回归预测模型的MATLAB代码，数据集维度为(753:，7)

好的，以下是一个简单的基于LSTM的七输入双输出回归预测模型的MATLAB代码：

% Load data
data = load('data.mat');
data = data.data;

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

% Normalize data
[train_data_norm, mu, sigma] = zscore(train_data);
test_data_norm = (test_data - mu)./sigma;

% Define input and output dimensions
num_features = 7;
num_outputs = 2;

% Define LSTM network architecture
num_hidden_units = 100;
layers = [ ...
    sequenceInputLayer(num_features)
    lstmLayer(num_hidden_units,'OutputMode','last')
    fullyConnectedLayer(num_outputs)
    regressionLayer];

% Define training options
options = trainingOptions('adam', ...
    'MaxEpochs',50, ...
    'MiniBatchSize',32, ...
    'InitialLearnRate',0.001, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropFactor',0.1, ...
    'LearnRateDropPeriod',10, ...
    'GradientThreshold',1, ...
    'Verbose',0);

% Train LSTM network
XTrain = train_data_norm(:,1:num_features);
YTrain = train_data_norm(:,num_features+1:end);
net = trainNetwork(XTrain',YTrain',layers,options);

% Test LSTM network
XTest = test_data_norm(:,1:num_features);
YTest = test_data_norm(:,num_features+1:end);
YPred = predict(net,XTest');
YPred = YPred'.*sigma(num_features+1:end) + mu(num_features+1:end);

% Plot results
figure;
subplot(2,1,1);
plot(YTest(:,1),'b');
hold on;
plot(YPred(:,1),'r');
title('Output 1');
legend('Actual','Predicted');
subplot(2,1,2);
plot(YTest(:,2),'b');
hold on;
plot(YPred(:,2),'r');
title('Output 2');
legend('Actual','Predicted');

在代码中，我们首先加载数据，并将其拆分为训练集和测试集。然后，我们对训练数据进行标准化处理，以便在训练期间更好地训练模型。接下来，我们定义了LSTM网络的架构，包括输入层、LSTM层、全连接层和回归层。我们还定义了训练选项，包括最大时期数、批量大小、初始学习率等。然后我们使用trainNetwork函数来训练LSTM网络。最后，我们使用predict函数来测试LSTM网络，并绘制实际和预测输出的图形。