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1 <html>
2 <head>
3 <title>
4 Netlab Reference Manual rbffwd
5 </title>
6 </head>
7 <body>
8 <H1> rbffwd
9 </H1>
10 <h2>
11 Purpose
12 </h2>
13 Forward propagation through RBF network with linear outputs.
14
15 <p><h2>
16 Synopsis
17 </h2>
18 <PRE>
19 a = rbffwd(net, x)
20 function [a, z, n2] = rbffwd(net, x)
21 </PRE>
22
23
24 <p><h2>
25 Description
26 </h2>
27 <CODE>a = rbffwd(net, x)</CODE> takes a network data structure
28 <CODE>net</CODE> and a matrix <CODE>x</CODE> of input
29 vectors and forward propagates the inputs through the network to generate
30 a matrix <CODE>a</CODE> of output vectors. Each row of <CODE>x</CODE> corresponds to one
31 input vector and each row of <CODE>a</CODE> contains the corresponding output vector.
32 The activation function that is used is determined by <CODE>net.actfn</CODE>.
33
34 <p><CODE>[a, z, n2] = rbffwd(net, x)</CODE> also generates a matrix <CODE>z</CODE> of
35 the hidden unit activations where each row corresponds to one pattern.
36 These hidden unit activations represent the <CODE>design matrix</CODE> for
37 the RBF. The matrix <CODE>n2</CODE> is the squared distances between each
38 basis function centre and each pattern in which each row corresponds
39 to a data point.
40
41 <p><h2>
42 Examples
43 </h2>
44 <PRE>
45
46 [a, z] = rbffwd(net, x);
47
48 <p>temp = pinv([z ones(size(x, 1), 1)]) * t;
49 net.w2 = temp(1: nd(2), :);
50 net.b2 = temp(size(x, nd(2)) + 1, :);
51 </PRE>
52
53 Here <CODE>x</CODE> is the input data, <CODE>t</CODE> are the target values, and we use the
54 pseudo-inverse to find the output weights and biases.
55
56 <p><h2>
57 See Also
58 </h2>
59 <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbftrain.htm">rbftrain</a></CODE>, <CODE><a href="rbfunpak.htm">rbfunpak</a></CODE><hr>
60 <b>Pages:</b>
61 <a href="index.htm">Index</a>
62 <hr>
63 <p>Copyright (c) Ian T Nabney (1996-9)
64
65
66 </body>
67 </html>