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回归预测 | MATLAB实现CNN-BiLSTM卷积双向长短期记忆神经网络多输入多输出


回归预测 | MATLAB实现CNN-BiLSTM卷积双向长短期记忆神经网络多输入多输出


目录

  • 回归预测 | MATLAB实现CNN-BiLSTM卷积双向长短期记忆神经网络多输入多输出
  • 预测效果
  • 基本介绍
  • 程序设计
  • 往期精彩
  • 参考资料


预测效果

回归预测 | MATLAB实现CNN-BiLSTM卷积双向长短期记忆神经网络多输入多输出_卷积双向长短期记忆神经网络


回归预测 | MATLAB实现CNN-BiLSTM卷积双向长短期记忆神经网络多输入多输出_回归预测_02


回归预测 | MATLAB实现CNN-BiLSTM卷积双向长短期记忆神经网络多输入多输出_回归预测_03


回归预测 | MATLAB实现CNN-BiLSTM卷积双向长短期记忆神经网络多输入多输出_CNN-BiLSTM_04


回归预测 | MATLAB实现CNN-BiLSTM卷积双向长短期记忆神经网络多输入多输出_卷积双向长短期记忆神经网络_05

基本介绍

MATLAB实现CNN-BiLSTM卷积双向长短期记忆神经网络多输入多输出,运行环境Matlab2020及以上。采用特征融合的方法,通过卷积网络提取出浅层特征与深层特征并进行联接,对特征通过卷积进行融合,将获得的矢量信息输入LSTM单元。

程序设计

  • 完整程序和数据私信博主。

%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%  创建"CNN-BiLSTM"模型
    layers = [...
        % 输入特征
        sequenceInputLayer([numFeatures 1 1],'Name','input')
        sequenceFoldingLayer('Name','fold')
        % CNN特征提取
        convolution2dLayer(FiltZise,32,'Padding','same','WeightsInitializer','he','Name','conv','DilationFactor',1);
        batchNormalizationLayer('Name','bn')
        eluLayer('Name','elu')
        averagePooling2dLayer(1,'Stride',FiltZise,'Name','pool1')
        % 展开层
        sequenceUnfoldingLayer('Name','unfold')
        % 平滑层
        flattenLayer('Name','flatten')
        % BiLSTM特征学习
        bilstmLayer(128,'Name','lstm1','RecurrentWeightsInitializer','He','InputWeightsInitializer','He')
        dropoutLayer(0.25,'Name','drop1')
        % BiLSTM输出
        bilstmLayer(32,'OutputMode',"last",'Name','bil4','RecurrentWeightsInitializer','He','InputWeightsInitializer','He')
        dropoutLayer(0.25,'Name','drop2')
        % 全连接层
        fullyConnectedLayer(numResponses,'Name','fc')
        regressionLayer('Name','output')    ];

    layers = layerGraph(layers);
    layers = connectLayers(layers,'fold/miniBatchSize','unfold/miniBatchSize');
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 训练选项
if gpuDeviceCount>0
    mydevice = 'gpu';
else
    mydevice = 'cpu';
end
    options = trainingOptions('adam', ...
        'MaxEpochs',MaxEpochs, ...
        'MiniBatchSize',MiniBatchSize, ...
        'GradientThreshold',1, ...
        'InitialLearnRate',learningrate, ...
        'LearnRateSchedule','piecewise', ...
        'LearnRateDropPeriod',56, ...
        'LearnRateDropFactor',0.25, ...
        'L2Regularization',1e-3,...
        'GradientDecayFactor',0.95,...
        'Verbose',false, ...
        'Shuffle',"every-epoch",...
        'ExecutionEnvironment',mydevice,...
        'Plots','training-progress');

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