Mercurial > hg > camir-aes2014

diff toolboxes/FullBNT-1.0.7/nethelp3.3/demrbf1.htm @ 0:e9a9cd732c1e tip
first hg version after svn
author: wolffd
date: Tue, 10 Feb 2015 15:05:51 +0000
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+<html>
+<head>
+<title>
+Netlab Reference Manual demrbf1
+</title>
+</head>
+<body>
+<H1> demrbf1
+</H1>
+<h2>
+Purpose
+</h2>
+Demonstrate simple regression using a radial basis function network.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+demrbf1</PRE>
+
+
+<p><h2>
+Description
+</h2>
+The problem consists of one input variable <CODE>x</CODE> and one target variable 
+<CODE>t</CODE> with data generated by sampling <CODE>x</CODE> at equal intervals and then 
+generating target data by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian 
+noise. This data is the same as that used in demmlp1.
+
+<p>Three different RBF networks (with different activation functions)
+are trained in two stages. First, a Gaussian mixture model is trained using
+the EM algorithm, and the centres of this model are used to set the centres
+of the RBF.  Second, the output weights (and biases) are determined using the
+pseudo-inverse of the design matrix.
+
+<p><h2>
+See Also
+</h2>
+<CODE><a href="demmlp1.htm">demmlp1</a></CODE>, <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbffwd.htm">rbffwd</a></CODE>, <CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmmem.htm">gmmem</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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