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<html>
<head>
<title>
Netlab Reference Manual rbffwd
</title>
</head>
<body>
<H1> rbffwd
</H1>
<h2>
Purpose
</h2>
Forward propagation through RBF network with linear outputs.
<p><h2>
Synopsis
</h2>
<PRE>
a = rbffwd(net, x)
function [a, z, n2] = rbffwd(net, x)
</PRE>
<p><h2>
Description
</h2>
<CODE>a = rbffwd(net, x)</CODE> takes a network data structure
<CODE>net</CODE> and a matrix <CODE>x</CODE> of input
vectors and forward propagates the inputs through the network to generate
a matrix <CODE>a</CODE> of output vectors. Each row of <CODE>x</CODE> corresponds to one
input vector and each row of <CODE>a</CODE> contains the corresponding output vector.
The activation function that is used is determined by <CODE>net.actfn</CODE>.
<p><CODE>[a, z, n2] = rbffwd(net, x)</CODE> also generates a matrix <CODE>z</CODE> of
the hidden unit activations where each row corresponds to one pattern.
These hidden unit activations represent the <CODE>design matrix</CODE> for
the RBF. The matrix <CODE>n2</CODE> is the squared distances between each
basis function centre and each pattern in which each row corresponds
to a data point.
<p><h2>
Examples
</h2>
<PRE>
[a, z] = rbffwd(net, x);
<p>temp = pinv([z ones(size(x, 1), 1)]) * t;
net.w2 = temp(1: nd(2), :);
net.b2 = temp(size(x, nd(2)) + 1, :);
</PRE>
Here <CODE>x</CODE> is the input data, <CODE>t</CODE> are the target values, and we use the
pseudo-inverse to find the output weights and biases.
<p><h2>
See Also
</h2>
<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbftrain.htm">rbftrain</a></CODE>, <CODE><a href="rbfunpak.htm">rbfunpak</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
<hr>
<p>Copyright (c) Ian T Nabney (1996-9)
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