📄 demrbf1.htm
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<html><head><title>Netlab Reference Manual demrbf1</title></head><body><H1> demrbf1</H1><h2>Purpose</h2>Demonstrate simple regression using a radial basis function network.<p><h2>Synopsis</h2><PRE>demrbf1</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. This data is the same as that used in demmlp1.<p>Three different RBF networks (with different activation functions)are trained in two stages. First, a Gaussian mixture model is trained usingthe EM algorithm, and the centres of this model are used to set the centresof the RBF. Second, the output weights (and biases) are determined using thepseudo-inverse of the design matrix.<p><h2>See Also</h2><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><b>Pages:</b><a href="index.htm">Index</a><hr><p>Copyright (c) Ian T Nabney (1996-9)</body></html>⌨️ 快捷键说明
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