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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual rbfbkp
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> rbfbkp
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 Backpropagate gradient of error function for RBF network.
wolffd@0 14
wolffd@0 15 <p><h2>
wolffd@0 16 Synopsis
wolffd@0 17 </h2>
wolffd@0 18 <PRE>
wolffd@0 19 g = rbfbkp(net, x, z, n2, deltas)</PRE>
wolffd@0 20
wolffd@0 21
wolffd@0 22 <p><h2>
wolffd@0 23 Description
wolffd@0 24 </h2>
wolffd@0 25 <CODE>g = rbfbkp(net, x, z, n2, deltas)</CODE> takes a network data structure
wolffd@0 26 <CODE>net</CODE> together with a matrix <CODE>x</CODE> of input vectors, a matrix
wolffd@0 27 <CODE>z</CODE> of hidden unit activations, a matrix <CODE>n2</CODE> of the squared
wolffd@0 28 distances between centres and inputs, and a matrix <CODE>deltas</CODE> of the
wolffd@0 29 gradient of the error function with respect to the values of the
wolffd@0 30 output units (i.e. the summed inputs to the output units, before the
wolffd@0 31 activation function is applied). The return value is the gradient
wolffd@0 32 <CODE>g</CODE> of the error function with respect to the network
wolffd@0 33 weights. Each row of <CODE>x</CODE> corresponds to one input vector.
wolffd@0 34
wolffd@0 35 <p>This function is provided so that the common backpropagation algorithm
wolffd@0 36 can be used by RBF network models to compute
wolffd@0 37 gradients for the output values (in <CODE>rbfderiv</CODE>) as well as standard error
wolffd@0 38 functions.
wolffd@0 39
wolffd@0 40 <p><h2>
wolffd@0 41 See Also
wolffd@0 42 </h2>
wolffd@0 43 <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfderiv.htm">rbfderiv</a></CODE><hr>
wolffd@0 44 <b>Pages:</b>
wolffd@0 45 <a href="index.htm">Index</a>
wolffd@0 46 <hr>
wolffd@0 47 <p>Copyright (c) Ian T Nabney (1996-9)
wolffd@0 48
wolffd@0 49
wolffd@0 50 </body>
wolffd@0 51 </html>