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<html> <head> <title> Netlab Reference Manual demmlp1 </title> </head> <body> <H1> demmlp1 </H1> <h2> Purpose </h2> Demonstrate simple regression using a multi-layer perceptron <p><h2> Synopsis </h2> <PRE> demmlp1</PRE> <p><h2> Description </h2> The problem consists of one input variable <CODE>x</CODE> and one target variable <CODE>t</CODE> with data generated by sampling <CODE>x</CODE> at equal intervals and then generating target data by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian noise. A 2-layer network with linear outputs is trained by minimizing a sum-of-squares error function using the scaled conjugate gradient optimizer. <p><h2> See Also </h2> <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> <b>Pages:</b> <a href="index.htm">Index</a> <hr> <p>Copyright (c) Ian T Nabney (1996-9) </body> </html>