# rbfgrad

## Purpose

Evaluate gradient of error function for RBF network.

## Synopsis

    g = rbfgrad(net, x, t)
    [g, gdata, gprior] = rbfgrad(net, x, t)

## Description

`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.

## See Also

`rbf`, `rbffwd`, `rbferr`, `rbfpak`, `rbfunpak`, `rbfbkp`

Pages: Index

Copyright (c) Ian T Nabney (1996-9)