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author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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+<html>
+<head>
+<title>
+Netlab Reference Manual rbfbkp
+</title>
+</head>
+<body>
+<H1> rbfbkp
+</H1>
+<h2>
+Purpose
+</h2>
+Backpropagate gradient of error function for RBF network.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+g = rbfbkp(net, x, z, n2, deltas)</PRE>
+
+
+<p><h2>
+Description
+</h2>
+<CODE>g = rbfbkp(net, x, z, n2, deltas)</CODE> takes a network data structure
+<CODE>net</CODE> together with a matrix <CODE>x</CODE> of input vectors, a matrix 
+<CODE>z</CODE> of hidden unit activations, a matrix <CODE>n2</CODE> of the squared
+distances between centres and inputs, and a matrix <CODE>deltas</CODE> 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
+<CODE>g</CODE> of the error function with respect to the network
+weights. Each row of <CODE>x</CODE> corresponds to one input vector.
+
+<p>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 <CODE>rbfderiv</CODE>) as well as standard error
+functions.
+
+<p><h2>
+See Also
+</h2>
+<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>
+<b>Pages:</b>
+<a href="index.htm">Index</a>
+<hr>
+<p>Copyright (c) Ian T Nabney (1996-9)
+
+
+</body>
+</html>
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