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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual demmlp1
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> demmlp1
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 Demonstrate simple regression using a multi-layer perceptron
wolffd@0 14
wolffd@0 15 <p><h2>
wolffd@0 16 Synopsis
wolffd@0 17 </h2>
wolffd@0 18 <PRE>
wolffd@0 19 demmlp1</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. A 2-layer network with linear outputs is trained by minimizing a
wolffd@0 29 sum-of-squares error function using the scaled conjugate gradient optimizer.
wolffd@0 30
wolffd@0 31 <p><h2>
wolffd@0 32 See Also
wolffd@0 33 </h2>
wolffd@0 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>
wolffd@0 35 <b>Pages:</b>
wolffd@0 36 <a href="index.htm">Index</a>
wolffd@0 37 <hr>
wolffd@0 38 <p>Copyright (c) Ian T Nabney (1996-9)
wolffd@0 39
wolffd@0 40
wolffd@0 41 </body>
wolffd@0 42 </html>