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1 <html>
2 <head>
3 <title>
4 Netlab Reference Manual rbfderiv
5 </title>
6 </head>
7 <body>
8 <H1> rbfderiv
9 </H1>
10 <h2>
11 Purpose
12 </h2>
13 Evaluate derivatives of RBF network outputs with respect to weights.
14
15 <p><h2>
16 Synopsis
17 </h2>
18 <PRE>
19 g = rbfderiv(net, x)</PRE>
20
21
22 <p><h2>
23 Description
24 </h2>
25 <CODE>g = rbfderiv(net, x)</CODE> takes a network data structure <CODE>net</CODE>
26 and a matrix of input vectors <CODE>x</CODE> and returns a three-index matrix
27 <CODE>g</CODE> whose <CODE>i</CODE>, <CODE>j</CODE>, <CODE>k</CODE> element contains the
28 derivative of network output <CODE>k</CODE> with respect to weight or bias
29 parameter <CODE>j</CODE> for input pattern <CODE>i</CODE>. The ordering of the
30 weight and bias parameters is defined by <CODE>rbfunpak</CODE>. This
31 function also takes into account any mask in the network data structure.
32
33 <p><h2>
34 See Also
35 </h2>
36 <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfbkp.htm">rbfbkp</a></CODE><hr>
37 <b>Pages:</b>
38 <a href="index.htm">Index</a>
39 <hr>
40 <p>Copyright (c) Ian T Nabney (1996-9)
41
42
43 </body>
44 </html>