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author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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wolffd@0 | 1 <html> |
wolffd@0 | 2 <head> |
wolffd@0 | 3 <title> |
wolffd@0 | 4 Netlab Reference Manual demrbf1 |
wolffd@0 | 5 </title> |
wolffd@0 | 6 </head> |
wolffd@0 | 7 <body> |
wolffd@0 | 8 <H1> demrbf1 |
wolffd@0 | 9 </H1> |
wolffd@0 | 10 <h2> |
wolffd@0 | 11 Purpose |
wolffd@0 | 12 </h2> |
wolffd@0 | 13 Demonstrate simple regression using a radial basis function network. |
wolffd@0 | 14 |
wolffd@0 | 15 <p><h2> |
wolffd@0 | 16 Synopsis |
wolffd@0 | 17 </h2> |
wolffd@0 | 18 <PRE> |
wolffd@0 | 19 demrbf1</PRE> |
wolffd@0 | 20 |
wolffd@0 | 21 |
wolffd@0 | 22 <p><h2> |
wolffd@0 | 23 Description |
wolffd@0 | 24 </h2> |
wolffd@0 | 25 The problem consists of one input variable <CODE>x</CODE> and one target variable |
wolffd@0 | 26 <CODE>t</CODE> with data generated by sampling <CODE>x</CODE> at equal intervals and then |
wolffd@0 | 27 generating target data by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian |
wolffd@0 | 28 noise. This data is the same as that used in demmlp1. |
wolffd@0 | 29 |
wolffd@0 | 30 <p>Three different RBF networks (with different activation functions) |
wolffd@0 | 31 are trained in two stages. First, a Gaussian mixture model is trained using |
wolffd@0 | 32 the EM algorithm, and the centres of this model are used to set the centres |
wolffd@0 | 33 of the RBF. Second, the output weights (and biases) are determined using the |
wolffd@0 | 34 pseudo-inverse of the design matrix. |
wolffd@0 | 35 |
wolffd@0 | 36 <p><h2> |
wolffd@0 | 37 See Also |
wolffd@0 | 38 </h2> |
wolffd@0 | 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> |
wolffd@0 | 40 <b>Pages:</b> |
wolffd@0 | 41 <a href="index.htm">Index</a> |
wolffd@0 | 42 <hr> |
wolffd@0 | 43 <p>Copyright (c) Ian T Nabney (1996-9) |
wolffd@0 | 44 |
wolffd@0 | 45 |
wolffd@0 | 46 </body> |
wolffd@0 | 47 </html> |