g = rbfgrad(net, x, t) [g, gdata, gprior] = rbfgrad(net, x, t)
g = rbfgrad(net, x, t) takes a network data structure net
together with a matrix x of input
vectors and a matrix t of target vectors, and evaluates the gradient
g of the error function with respect to the network weights (i.e.
including the hidden unit parameters). The error
function is sum of squares.
Each row of x corresponds to one
input vector and each row of t contains the corresponding target vector.
If the output function is 'neuroscale' then the gradient is only
computed for the output layer weights and biases.
[g, gdata, gprior] = rbfgrad(net, x, t) also returns separately
the data and prior contributions to the gradient. In the case of
multiple groups in the prior, gprior is a matrix with a row
for each group and a column for each weight parameter.
rbf, rbffwd, rbferr, rbfpak, rbfunpak, rbfbkpCopyright (c) Ian T Nabney (1996-9)