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authorziejd22017-09-28 15:04:40 -0500
committerziejd22017-09-28 15:04:40 -0500
commit8070dc963753142bb86c4ed698d91fd623ed28e7 (patch)
treed0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/demrbf1.htm
parent7cc31810d53176e805532b2789955f4eedbce6bb (diff)
downloadBNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02
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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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