多输入多输出 | MATLAB实现BiTCN(双向时间卷积神经网络)多输入多输出预测

预测效果

基本介绍

Matlab实现BiTCN双向时间卷积神经网络多输入多输出预测
1.data为数据集，10个输入特征，3个输出变量。
2.main.m为主程序文件，其余为函数文件，无需运行，运行环境Matlab2021b及以上。
3.命令窗口输出MBE、MAE、MAPE、RMSE和R2，可在下载区获取数据和程序内容。

程序设计

• 完整源码和数据获取方式：私信博主回复MATLAB实现BiTCN(双向时间卷积神经网络)多输入多输出预测；

  % 连接卷积层到残差块
    lgraph = connectLayers(lgraph, outputName, "conv1_" + i);
 
    % 创建 TCN反向支路flip网络结构
    Fliplayers = [
        FlipLayer("flip_" + i)                                                                                               % 反向翻转
        convolution1dLayer(1, numFilters, Name = "convSkip_"+i);                                                             % 反向残差连接
        convolution1dLayer(filterSize, numFilters, DilationFactor = dilationFactor, Padding = "causal", Name="conv2_" + i)   % 一维卷积层
        layerNormalizationLayer                                                                                              % 层归一化
        spatialDropoutLayer(dropoutFactor)                                                                                   % 空间丢弃层
        convolution1dLayer(filterSize, numFilters, DilationFactor = dilationFactor, Padding = "causal")                      % 一维卷积层
        layerNormalizationLayer                                                                                              % 层归一化
        reluLayer                                                                                                            % 激活层
        spatialDropoutLayer(dropoutFactor, Name="drop" + i)                                                                  % 空间丢弃层
    ];
 
    % 添加 flip 网络结构到网络
    lgraph = addLayers(lgraph, Fliplayers);
 
    % 连接 flip 卷积层到残差块
    lgraph = connectLayers(lgraph, outputName, "flip_" + i);
    lgraph = connectLayers(lgraph, "drop" + i, "add_" + i + "/in3");
    lgraph = connectLayers(lgraph, "convSkip_"+i, "add_" + i + "/in4");
    % 残差连接 -- 首层
    if i == 1
        % 建立残差卷积层
        % Include convolution in first skip connection.
        layer = convolution1dLayer(1,numFilters,Name="convSkip");

        lgraph = addLayers(lgraph,layer);
        lgraph = connectLayers(lgraph,outputName,"convSkip");
        lgraph = connectLayers(lgraph,"convSkip","add_" + i + "/in2");
    else
        lgraph = connectLayers(lgraph,outputName,"add_" + i + "/in2");
    end
    
    % Update layer output name.
    outputName = "add_" + i;
end

参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/128247182
[2] https://blog.csdn.net/kjm13182345320/article/details/126043107