a = rbffwd(net, x) function [a, z, n2] = rbffwd(net, x)
a = rbffwd(net, x) takes a network data structure
net and a matrix x of input
vectors and forward propagates the inputs through the network to generate
a matrix a of output vectors. Each row of x corresponds to one
input vector and each row of a contains the corresponding output vector.
The activation function that is used is determined by net.actfn.
[a, z, n2] = rbffwd(net, x) also generates a matrix z of
the hidden unit activations where each row corresponds to one pattern.
These hidden unit activations represent the design matrix for
the RBF. The matrix n2 is the squared distances between each
basis function centre and each pattern in which each row corresponds
to a data point.
[a, z] = rbffwd(net, x);Heretemp = pinv([z ones(size(x, 1), 1)]) * t; net.w2 = temp(1: nd(2), :); net.b2 = temp(size(x, nd(2)) + 1, :);
x is the input data, t are the target values, and we use the
pseudo-inverse to find the output weights and biases.
rbf, rbferr, rbfgrad, rbfpak, rbftrain, rbfunpakCopyright (c) Ian T Nabney (1996-9)