% -*- texinfo -*-
% @deftypefn {Function File} {@var{net}} = newff (@var{Pr},@var{ss},@var{trf},@var{btf},@var{blf},@var{pf})
% @code{newff} create a feed-forward backpropagation network
%
% @example
% Pr - R x 2 matrix of min and max values for R input elements
% Ss - 1 x Ni row vector with size of ith layer, for N layers
% trf - 1 x Ni list with transfer function of ith layer,
% default = 'tansig'
% btf - Batch network training function,
% default = 'trainlm'
% blf - Batch weight/bias learning function,
% default = 'learngdm'
% pf - Performance function,
% default = 'mse'.
% @end example
%
% @example
% EXAMPLE 1
% Pr = [0.1 0.8; 0.1 0.75; 0.01 0.8];
% it's a 3 x 2 matrix, this means 3 input neurons
%
% net = newff(Pr, [4 1], @{'tansig','purelin'@}, 'trainlm', 'learngdm', 'mse');
% @end example
%
% @end deftypefn