帮我写一个基于LSTM的多输入双输出回归预测模型的MATLAB代码

好的，以下是一个基于LSTM的多输入双输出回归预测模型的MATLAB代码：

% Load data
load data.mat

% Define input data
X = [input1, input2, input3];

% Define output data
Y = [output1, output2];

% Normalize data
Xnorm = normalize(X);
Ynorm = normalize(Y);

% Split data into training and testing sets
trainRatio = 0.7;
valRatio = 0.15;
testRatio = 0.15;

[trainInd,valInd,testInd] = dividerand(size(Xnorm,1),trainRatio,valRatio,testRatio);

Xtrain = Xnorm(trainInd,:);
Ytrain = Ynorm(trainInd,:);
Xval = Xnorm(valInd,:);
Yval = Ynorm(valInd,:);
Xtest = Xnorm(testInd,:);
Ytest = Ynorm(testInd,:);

% Define LSTM network architecture
numFeatures = size(X,2);
numResponses = size(Y,2);
numHiddenUnits = 200;

layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits,'OutputMode','sequence')
    dropoutLayer(0.2)
    lstmLayer(numHiddenUnits,'OutputMode','last')
    dropoutLayer(0.2)
    fullyConnectedLayer(numResponses)
    regressionLayer];

% Set training options
maxEpochs = 100;
miniBatchSize = 64;
initialLearnRate = 0.001;

options = trainingOptions('adam', ...
    'MaxEpochs',maxEpochs, ...
    'MiniBatchSize',miniBatchSize, ...
    'InitialLearnRate',initialLearnRate, ...
    'GradientThreshold',1, ...
    'Shuffle','every-epoch', ...
    'ValidationData',{Xval,Yval}, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress');

% Train LSTM network
net = trainNetwork(Xtrain,Ytrain,layers,options);

% Test LSTM network
YPred = predict(net,Xtest);
YPred = denormalize(YPred,Y);

% Evaluate performance
rmse = sqrt(mean((Ytest - YPred).^2));
r2 = 1 - sum((Ytest - YPred).^2)/sum((Ytest - mean(Ytest)).^2);

disp(['RMSE: ', num2str(rmse)])
disp(['R2 score: ', num2str(r2)])

需要注意的是，上述代码需要配合一个名为`data.mat`的数据文件使用，其中包含了输入数据（`input1`、`input2`、`input3`）和输出数据（`output1`、`output2`）。另外，代码中涉及到了数据归一化和反归一化的操作，这是为了确保输入输出数据在相同的尺度上，从而提高模型的训练和预测效果。