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From: Greg Heath <heath@alumni.brown.edu>
Newsgroups: comp.soft-sys.matlab
Subject: Re: Backpropagation Neural network Code problem
Date: Tue, 17 Mar 2009 12:13:33 -0700 (PDT)
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On Mar 17, 2:40 pm, Greg Heath <he...@alumni.brown.edu> wrote:
> On Mar 16, 11:46 pm, "Adeel " <neoresearc...@gmail.com> wrote:
> > 1. OK i will insert much more comments in the code.
>
> > 2. You are right that the other layer is output layer, But I had two la=
yers, because output layer had weights (plus bias) on them. Not to mention =
the activation function etc.

Two important points on terminology:

1. The input layer is not a neuron layer.
2. The output layer is not a hidden layer. (correct your comment in
the code)

> > 3. The name of this file is element wise, that is element by element ev=
ery step, Just a precaution to safe guard against any malfunction or mistak=
e in the matrix multiplication done by me. (Although i had created one file=
 using matrix multiplication).
>
> > 4. OK i agree i did not know that. Plus i did not know what "logarithmi=
c sigmoid" is. Are u talking about bipolar sigmoid. ( I have not used tanh,=
 I had used bipolar sigmoid.). In the book "Fundamentals ofneuralnetworks b=
y laurene Fausett of pearson education" They had used for both layer the bi=
polar sigmoid. and according to the text they had converge the weight using=
 the bipolar sigmoid as an activation function on both the layers. (unless =
i had understood it incorrectly. According to them they had solve the xor p=
roblem solution in binary representation, to get converge in 3000 epochs.. =
Mine never did)

logsig(x) =3D 1/(1+exp(-x))
tanh(x/2) =3D 2*logsig(x)-1

You might be interested in

http://groups.google.com/group/comp.ai.neural-nets/
msg/1c33c39b186dd026?hl=3Den

Note the learning times using the batch L-M algorithm.

Hope this helps.

Greg