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| author | wolffd |
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| date | Tue, 10 Feb 2015 15:05:51 +0000 |
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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> |