Multi Input Neural Network with delayed target input.

Question

Emil 2013-3-13

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/67045-multi-input-neural-network-with-delayed-target-input

评论： ASIF IQBAL 2022-10-27

I'd like to design a neural network that takes multiple inputs, one of them being a delayed average of the target output.

The below "example" code will make a diagram with what I hope is a real time (no delay) inputs for x1,x3,x4,x5,x6 & a delay of 8 steps in time for y(t). With that delayed step in time made up of an moving average of 1:8 steps.

Thanks for your feedback...

    inputDelays = 1:1;
    feedbackDelays = 1:1;
    hiddenLayerSize = 4;
    net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
    net.numInputs=6;
    net.inputs{1}.name='x1';
    net.inputs{2}.name='y';
    net.inputs{3}.name='x3';
    net.inputs{4}.name='x4';
    net.inputs{5}.name='x5';
    net.inputs{6}.name='x6';
    net.inputConnect=logical([1 1 1 1 1 1; 0 0 0 0 0 0]);
    net.inputWeights{1,1}.delays=0;
    % delayed 8 steps in time wrt other inputWeights 
    % when appled value is avg of 1:8 steps
    net.inputWeights{1,2}.delays=1:8;
    net.inputWeights{1,3}.delays=0;
    net.inputWeights{1,4}.delays=0;
    net.inputWeights{1,5}.delays=0;
    net.inputWeights{1,6}.delays=0;
    view(net)

1 个评论
显示 -1更早的评论隐藏 -1更早的评论

ASIF IQBAL 2022-10-27

how we can use different lag input combination in any machine learning algorithm?

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Greg Heath 2013-3-15

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/67045-multi-input-neural-network-with-delayed-target-input#answer_78726

To be consistent with MATLAB notation, rename the target t and the output y.

What are the significant correlation delays in the target autocorrelation function?

What are the significant correlation delays in the 5 crosscorrelation functions between the target and the 5 inputs?

Do you realize that

a. For nondelayed inputs min(ID) = 0

b. All 5 inputs will have the same delays but the output feedback delays can be different.

c. Multiple inputs and delays all share the same delay buffer that has the length LDB = max( [ 1+max(ID), max(FD) ])

d. All delays <= LBD do not have to be included in ID or FD

Don't introduce the complication of an 8 point moving average feedback delay input. Just put the delays in the buffer and let the training algorithm decide how to weight them.

Hope this helps.

Thank you for formally accepting my answer

Greg