MATLAB: How many degrees should be counted for computing the adjusted R^2 of a neural network

adusted r^2

Hi everyone,
I am trying to calculate the adusted R^2 of a network.Thus I should take into account the number of parameters of the network. The number of parameters delivered from a network with k inputs, i hidden units, 1 hidden layer kai n output should be equal to
k*l + l*n + bias hidden layer + bias in the output + k*n direct input-output connections.
Does anyone know if matlab has a bias in the output? Does anytone know if matlab calculates parameters for the direct input-output connections?
Thanks in advance

Best Answer

Consider N pairs of I-dimensional "I" nput vectors and O-dimensional "O"utput target vectors. If Ntrn is the number of training pairs, the number of training equations is
 Ntrneq = Ntrn*O
If SSEtrn is the sum of squared training subset errors, corresponding mean-square-error, MSEtrn is given by
 MSEtrn = SSEtrn/Ntrneq
For an I-H-O feedforward net, the number of unknown weight variables to be estimated by the training subset pairs is
 Nw = (I+1)*H+(H+1)*O = O + (I+O+1)*H
resulting in the number of training degrees-of-freedom
 Ntrndof = Ntrneq - Nw
Therefore, the DOF adjusted training set mean-square-error is given by
 MSEtrna = SSEtrn/ Ntrndof
I have posted zillions of examples in both the NEWSGROUP and ANSWERS. Some examples can be obtained with the search words
    greg MSEa
or
    greg MSEtrna
Hope this helps.
Thank you for formally accepting my answer
Greg
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