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Topic: Neural Networks weights and bias help
Replies: 18   Last Post: Jan 22, 2016 9:11 AM

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Greg Heath

Posts: 6,387
Registered: 12/7/04
Re: Neural Networks weights and bias help
Posted: Jan 20, 2016 2:54 PM
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"Vikas" wrote in message <n7oepr$sju$1@newscl01ah.mathworks.com>...
> "Greg Heath" <heath@alumni.brown.edu> wrote in message <kackrs$k0t$1@newscl01ah.mathworks.com>...
> > "Renan" wrote in message <kaa61v$hau$1@newscl01ah.mathworks.com>...
> > > Please, I´m in the same situation. Did you figure it out?
> >
> > What EXACTLY do you want to do?
> >
> > What version of MATLAB do you have?
> >
> > Please post the documentation you get from the command
> >
> > help newff
> >
> > Greg

>
> What's the difference between BTF (Backprop network training function, default = 'trainlm') and BLF (Backprop weight/bias learning function, default = 'learngdm') in the newff help document? Its some what confusing in the document, can you share your knowledge on this please?
>
> NEWFF(P,T,S,TF,BTF,BLF,PF,IPF,OPF,DDF) takes,
> P - RxQ1 matrix of Q1 representative R-element input vectors.
> T - SNxQ2 matrix of Q2 representative SN-element target vectors.
> Si - Sizes of N-1 hidden layers, S1 to S(N-1), default = [].
> (Output layer size SN is determined from T.)
> TFi - Transfer function of ith layer. Default is 'tansig' for
> hidden layers, and 'purelin' for output layer.
> BTF - Backprop network training function, default = 'trainlm'.
> BLF - Backprop weight/bias learning function, default = 'learngdm'.
> PF - Performance function, default = 'mse'.
> IPF - Row cell array of input processing functions.
> Default is {'fixunknowns','removeconstantrows','mapminmax'}.
> OPF - Row cell array of output processing functions.
> Default is {'removeconstantrows','mapminmax'}.
> DDF - Data division function, default = 'dividerand';


trainlm is the levenberg-marqardt training function

help trainlm
doc trainlm

learngdm is a backpropagation learning algorithm that uses gradient descent with momentum

help learngdm
doc traingdm

Happy reading!

Greg



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