machine-learningneural-networkbackpropagation
How to modify backpropagation for a standard multilayer network including a scalar gain at each layer?
Considering a standard multilayer network including a scalar gain at each layer. The net input at layer m would be computed as : n^m = β^m [W^m α^m − 1 + b^m]
where β^m is the scalar gain at layer m . This gain would be trained like the weights and biases of the network.
How can I modify the backpropagation algorithm for this new network ?
What would be a new equation added to update β^m ?
This is an exercise from this book .
E11.13
Neural Network Design (2nd Edition) - Martin T. Hagan, Howard B. Demuth, Mark H. Beale, Orlando De Jesus
Solution
I have written the answer in LaTeX
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