MATLAB: Unfolding of recurrent neural nets for back-propagation training

neural networks

When training a layrecnet (I believe this is the right function to use for a "standard" recurrent neural network), and training e.g. by the default trainlm method, how many time-steps are unfolded for the back-propagation? Can this number be specified and if so, how?

Best Answer

You question doesn't make any sense to me. Have you

1. Read the documentation

 help layrecnet
 doc layrecnet

2. Searched "layrecnet" in previous postings of both the NEWSGROUP and ANSWERS?

Hope this helps.

Greg

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MATLAB: Sensitivity analysis, multilayer, feed-forward, back-propagation neural network using MATLAB.

For a simple MIMO MLP, the I/O relationship is

 [ I N ] = size(x);
 [ O N ] = size(y);
 y = b2 + LW*tanh(b1+IW*x);
 Just take gradients with respect to whatever parameter component you are interested in.

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: Unfold cell

>> vector = cell2mat(cell)

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MATLAB: Sensitivity analysis, multilayer, feed-forward, back-propagation neural network using MATLAB.

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