I use the concept
Number of training equations Ntrneq >> Number of unknown weights Nw
For a single layer I-H-O net with I-dimensional inputs, O-dimensional outputs and H hidden nodes,
Ntrneq = Ntrn*O % (Typically, Default Ntrn ~ 0.7*N)
Nw = (I+1)*H + (H+1)*O = O + (I+O+1)*H
Ntrn * O >> O + (I+O+1)*H
When Ntrn is too small and/or H is too large, trainlm is usually replaced by trainbr and/or other "overfitting" mitigations are available.
Hope this helps.
Thank you for formally accepting my answer
Greg
