How to build a neural network which is not Fully-connected with NN toolbox?
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Hi, I'm using NN toolbox to build my own network. The problem is that it seems that NN toolbox offers only fully-connected network. The image attached can be one example. Is there any way that I can build a neural network with disconnecting some weights?
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/189351/image.png)
1 comentario
Itay Hanoch
el 24 de Sept. de 2020
Hi,
I run into the same problem as you,
Trying to find a way to disconnect specific weight in a layer,
Did you you found a way to deal with this problem at the end?
Respuestas (1)
Greg Heath
el 13 de Abr. de 2018
The best approach is to find, via an exhaustive search within bounds, the minimum number of hidden nodes that will yield your desired result.
I have posted ZILLIONS of examples in both the NEWSGROUP (comp.soft-sys.matlab) and ANSWERS.
For
1. N I-dimensional "I"nputs yielding N O-dimensional "O"utputs
2. Default 0.7/0.15/0.15 data division
3. H hidden units in a default I-H-O node topology
Ntrneq ~ 0.7*N*O % No. of training equations
Nw = (I+1)*H+(H+1)*O % No. of unknown weights
Find the minimum number of hidden units that will guarantee
Ntrneq >= Nw
or
H <= (Ntrneq-O)/(I+O+1)
subject to the following target variance performance constraint on the error
error = target-output
mse(error) <= 0.01*var(target',1)
Hope this helps.
Thank you for formally accepting my answer
Greg
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