Re: neural networks normalization

"AFAQ AHMAD" <aa22@hw.ac.uk> wrote in message <mkpb7o$cf5$1@newscl01ah.mathworks.com>...
> Hi Ruben
> Thanks for your question.
> Please explain where I have to use "On the other hand, you can create your MLP and you can do later nn.inputs{1}.processFcns={} and nn.outputs{1}.processFcns={}." in the newff command or after training the network.
> Regards
> Afaq

This thread was started in May 2009 regarding version 2008b. Checking the help and doc documentation for 2014a

 >> help newff
   NEWFF Create a feed-forward back-propagation network.
 
   Obsoleted in R2010b NNET 7.0. Last used in R2010a NNET 6.0.4.
   The recommended function is FEEDFORWARDNET.

 However, FEEDFORWARDNET is a generic function that is automatically called by the specialized functions FITNET (regression and curve-fitting) and PATTERNNET (classification and pattern-recognition)

Using FITNET as an example:
 net = fitnet % No semicolon reveals

 >> inputprocessFcns = net.input.processFcns
 outputprocessFcns = net.output.processFcns

inputprocessFcns = 'removeconstantrows' 'mapminmax'
outputprocessFcns = 'removeconstantrows' 'mapminmax'

Therefore use the commands

net.inputprocessFcns = 'removeconstantrows' ;
net.outputprocessFcns = 'removeconstantrows' ;

similarly for PATTERNNET.

Hope this helps.

Greg