Relation between input data points and hyper parameters that needs to be tuned

 [ I N]  = size(input)
 [ O N ] = size(target)

 % (MATLAB DEFAULT)
   Ntst    = round(0.15*N)
   Nval    = Ntst
   Ntrn    = N-(Ntst+Nval)%  ~ 0.7*N

 % Design parameters 
   Ndes = Ntrn*O           % No. of design equations ~ 0.7*N*O
   H                       % No. of hidden nodes for I-H-O net
   Nw   = (I+1)*H+(H+1)*O  % No. of unknown weights

 Require  Ndes >= Nw   ==> H <= Hub = (Ntrn*O-O)/(I+O+1)
 Desire   Ndes >> Nw   ==> H << Hub

         MSE < = 0.01*var(target',1)   % Rsquare >= 0.99

My approach:

 1. Apply the requirement to the training data
 2. Loop over H to find the minimum H to satisfy the 
    requirement.