+ +net = rbf(nin, nhidden, nout, rbfunc) +net = rbf(nin, nhidden, nout, rbfunc, outfunc) +net = rbf(nin, nhidden, nout, rbfunc, outfunc, prior, beta) ++ + +
net = rbf(nin, nhidden, nout, rbfunc) constructs and initialises
+a radial basis function network returning a data structure net.
+The weights are all initialised with a zero mean, unit variance normal
+distribution, with the exception of the variances, which are set to one.
+This makes use of the Matlab function
+randn and so the seed for the random weight initialization can be
+set using randn('state', s) where s is the seed value. The
+activation functions are defined in terms of the distance between
+the data point and the corresponding centre. Note that the functions are
+computed to a convenient constant multiple: for example, the Gaussian
+is not normalised. (Normalisation is not needed as the function outputs
+are linearly combined in the next layer.)
+
+The fields in net are
+
+ + type = 'rbf' + nin = number of inputs + nhidden = number of hidden units + nout = number of outputs + nwts = total number of weights and biases + actfn = string defining hidden unit activation function: + 'gaussian' for a radially symmetric Gaussian function. + 'tps' for r^2 log r, the thin plate spline function. + 'r4logr' for r^4 log r. + outfn = string defining output error function: + 'linear' for linear outputs (default) and SoS error. + 'neuroscale' for Sammon stress measure. + c = centres + wi = squared widths (null for rlogr and tps) + w2 = second layer weight matrix + b2 = second layer bias vector ++ + +
net = rbf(nin, nhidden, nout, rbfund, outfunc) allows the user to
+specify the type of error function to be used. The field outfn
+is set to the value of this string. Linear outputs (for regression problems)
+and Neuroscale outputs (for topographic mappings) are supported.
+
+
net = rbf(nin, nhidden, nout, rbfunc, outfunc, prior, beta),
+in which prior is
+a scalar, allows the field net.alpha in the data structure
+net to be set, corresponding to a zero-mean isotropic Gaussian
+prior with inverse variance with value prior. Alternatively,
+prior can consist of a data structure with fields alpha
+and index, allowing individual Gaussian priors to be set over
+groups of weights in the network. Here alpha is a column vector
+in which each element corresponds to a separate group of weights,
+which need not be mutually exclusive. The membership of the groups is
+defined by the matrix indx in which the columns correspond to
+the elements of alpha. Each column has one element for each
+weight in the matrix, in the order defined by the function
+rbfpak, and each element is 1 or 0 according to whether the
+weight is a member of the corresponding group or not. A utility
+function rbfprior is provided to help in setting up the
+prior data structure.
+
+
net = rbf(nin, nhidden, nout, func, prior, beta) also sets the
+additional field net.beta in the data structure net, where
+beta corresponds to the inverse noise variance.
+
+
x through it.
++ +net = rbf(1, 5, 1, 'tps'); +[y, act] = rbffwd(net, x); ++ + +
rbferr, rbffwd, rbfgrad, rbfpak, rbftrain, rbfunpakCopyright (c) Ian T Nabney (1996-9) + + + + \ No newline at end of file -- cgit 1.4.1