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author Daniel Wolff
date Fri, 19 Aug 2016 13:07:06 +0200
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1 <html>
2 <head>
3 <title>
4 Netlab Reference Manual demrbf1
5 </title>
6 </head>
7 <body>
8 <H1> demrbf1
9 </H1>
10 <h2>
11 Purpose
12 </h2>
13 Demonstrate simple regression using a radial basis function network.
14
15 <p><h2>
16 Synopsis
17 </h2>
18 <PRE>
19 demrbf1</PRE>
20
21
22 <p><h2>
23 Description
24 </h2>
25 The problem consists of one input variable <CODE>x</CODE> and one target variable
26 <CODE>t</CODE> with data generated by sampling <CODE>x</CODE> at equal intervals and then
27 generating target data by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian
28 noise. This data is the same as that used in demmlp1.
29
30 <p>Three different RBF networks (with different activation functions)
31 are trained in two stages. First, a Gaussian mixture model is trained using
32 the EM algorithm, and the centres of this model are used to set the centres
33 of the RBF. Second, the output weights (and biases) are determined using the
34 pseudo-inverse of the design matrix.
35
36 <p><h2>
37 See Also
38 </h2>
39 <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>
40 <b>Pages:</b>
41 <a href="index.htm">Index</a>
42 <hr>
43 <p>Copyright (c) Ian T Nabney (1996-9)
44
45
46 </body>
47 </html>