多輸入多輸出 | MATLAB實現BiLSTM雙向長短期記憶神經網絡多輸入多輸出預測

預測效果

基本介紹

MATLAB實現BiLSTM雙向長短期記憶神經網絡多輸入多輸出預測，數據為多輸入多輸出預測數據，輸入10個特征，輸出3個變量,程序亂碼是由于版本不一致導致，可以用記事本打開復制到你的文件，運行環(huán)境MATLAB2018b及以上。命令窗口輸出MAE和R2，可在下載區(qū)獲取數據和程序內容。

程序設計

• 完整程序和數據下載方式(資源處直接下載)：MATLAB實現BiLSTM雙向長短期記憶神經網絡多輸入多輸出預測

%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
layers = [ ...
    sequenceInputLayer(numFeatures)
   
    fullyConnectedLayer(numResponses)
    regressionLayer];
options = trainingOptions('adam', ...
    'MaxEpochs',250, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',0.005, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropPeriod',125, ...
    'LearnRateDropFactor',0.2, ...
    'ExecutionEnvironment','cpu', ...
    'Verbose',0, ...
    'Plots','training-progress');
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------   
net = trainNetwork(XTrain,YTrain,layers,options);
dataTestStandardized = (dataTest - mu) / sig;
XTest = dataTestStandardized(1:end-1);
net = predictAndUpdateState(net,XTrain);
[net,YPred] = predictAndUpdateState(net,YTrain(end));
numTimeStepsTest = numel(XTest);
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

往期精彩

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MATLAB實現GRNN廣義回歸神經網絡多輸入多輸出預測
MATLAB實現GRU門控循環(huán)單元多輸入多輸出

參考資料

[1] https://blog.csdn.net/kjm13182345320/article/details/116377961
[2] https://blog.csdn.net/kjm13182345320/article/details/127931217
[3] https://blog.csdn.net/kjm13182345320/article/details/127894261文章來源地址http://www.zghlxwxcb.cn/news/detail-652580.html

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