g = 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.
rbf, rbffwd, rbferr, rbfpak, rbfunpak, rbftrainCopyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997)