# rbfbkp

## Purpose

Backpropagate gradient of error function for RBF network.

## Synopsis

    g = rbfbkp(net, x, z, n2, deltas)

## Description

`g = rbfbkp(net, x, z, n2, deltas)` takes a network data structure `net` together with a matrix `x` of input vectors, a matrix `z` of hidden unit activations, a matrix `n2` of the squared distances between centres and inputs, and a matrix `deltas` of the gradient of the error function with respect to the values of the output units (i.e. the summed inputs to the output units, before the activation function is applied). The return value is the gradient `g` of the error function with respect to the network weights. Each row of `x` corresponds to one input vector.

This function is provided so that the common backpropagation algorithm can be used by RBF network models to compute gradients for the output values (in `rbfderiv`) as well as standard error functions.

## See Also

`rbf`, `rbfgrad`, `rbfderiv`

Pages: Index

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