I = 85, O = 26, N = 130, H = 50
[ I N ] = size(input) % [ 85 130]
[ O N ] = size(target) % [ 26 130]
Ntrn = N -2*(0.15*N) % 91 training examples
Ntrneq= Ntrn*O % 2366 training equations
Nw = (I+1)*H+(H+1)*O % 5626 unknown weights
Hub = -1 + ceil( (Ntrneq-O) / ( I + O + 1 ) % 20
Try to lower H as much as possible. It should make your model more robust (w.r.t. noise, measurement error and unseen data). Typically, I try to look at ~10 different values for H and ~Ntrials = 10 different designs for each value of H to mitigate the probability of getting a poor set of initial weights.
I have many posts in the NEWSGROUP and ANSWERS. Search on
greg patternnet Ntrials
greg newpr Ntrials
for h = Hmin:dH:Hmax
....
for i = 1:Ntrials
....
end
end
Hope this helps,
Greg
