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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 demmlp1 | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> demmlp1 | |
| 9 </H1> | |
| 10 <h2> | |
| 11 Purpose | |
| 12 </h2> | |
| 13 Demonstrate simple regression using a multi-layer perceptron | |
| 14 | |
| 15 <p><h2> | |
| 16 Synopsis | |
| 17 </h2> | |
| 18 <PRE> | |
| 19 demmlp1</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. A 2-layer network with linear outputs is trained by minimizing a | |
| 29 sum-of-squares error function using the scaled conjugate gradient optimizer. | |
| 30 | |
| 31 <p><h2> | |
| 32 See Also | |
| 33 </h2> | |
| 34 <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> | |
| 35 <b>Pages:</b> | |
| 36 <a href="index.htm">Index</a> | |
| 37 <hr> | |
| 38 <p>Copyright (c) Ian T Nabney (1996-9) | |
| 39 | |
| 40 | |
| 41 </body> | |
| 42 </html> |