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<html><head><title>Netlab Reference Manual rbftrain</title></head><body><H1> rbftrain</H1><h2>Purpose</h2>Two stage training of RBF network.<p><h2>Description</h2><CODE>net = rbftrain(net, options, x, t)</CODE> uses a two stage trainingalgorithm to set the weights in the RBF model structure <CODE>net</CODE>.Each row of <CODE>x</CODE> corresponds to oneinput vector and each row of <CODE>t</CODE> contains the corresponding target vector.The centres are determined by fitting a Gaussian mixture modelwith circular covariances using the EM algorithm through a call to<CODE>rbfsetbf</CODE>. (The mixture model isinitialised using a small number of iterations of the K-means algorithm.)If the activation functions are Gaussians, then the basis function widthsare then set to the maximum inter-centre squared distance.<p>For linear outputs, the hidden to outputweights that give rise to the least squares solutioncan then be determined using the pseudo-inverse. For neuroscale outputs,the hidden to output weights are determined using the iterative shadowtargets algorithm. Although this two stageprocedure may not give solutions with as low an error as using general purpose non-linear optimisers, it is much faster.<p>The options vector may have two rows: if this is the case, then the second rowis passed to <CODE>rbfsetbf</CODE>, which allows the user to specify a differentnumber iterations for RBF and GMM training.The optional parameters to <CODE>rbftrain</CODE> have the following interpretations.<p><CODE>options(1)</CODE> is set to 1 to display error values during EM training.<p><CODE>options(2)</CODE> is a measure of the precision required for the valueof the weights <CODE>w</CODE> at the solution.<p><CODE>options(3)</CODE> is a measure of the precision required of the objectivefunction at the solution. Both this and the previous condition must besatisfied for termination.<p><CODE>options(5)</CODE> is set to 1 if the basis functions parameters should remainunchanged; default 0.<p><CODE>options(6)</CODE> is set to 1 if the output layer weights should be should set using PCA. This is only relevant for Neuroscale outputs; default 0.<p><CODE>options(14)</CODE> is the maximum number of iterations for the shadowtargets algorithm; default 100.<p><h2>Example</h2>The following example creates an RBF network and then trains it:<PRE>net = rbf(1, 4, 1, 'gaussian');options(1, :) = foptions;options(2, :) = foptions;options(2, 14) = 10; % 10 iterations of EMoptions(2, 5) = 1; % Check for covariance collapse in EMnet = rbftrain(net, options, x, t);</PRE><p><h2>See Also</h2><CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbffwd.htm">rbffwd</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbfunpak.htm">rbfunpak</a></CODE>, <CODE><a href="rbfsetbf.htm">rbfsetbf</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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