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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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1 <html> | |
2 <head> | |
3 <title> | |
4 Netlab Reference Manual rbfbkp | |
5 </title> | |
6 </head> | |
7 <body> | |
8 <H1> rbfbkp | |
9 </H1> | |
10 <h2> | |
11 Purpose | |
12 </h2> | |
13 Backpropagate gradient of error function for RBF network. | |
14 | |
15 <p><h2> | |
16 Synopsis | |
17 </h2> | |
18 <PRE> | |
19 g = rbfbkp(net, x, z, n2, deltas)</PRE> | |
20 | |
21 | |
22 <p><h2> | |
23 Description | |
24 </h2> | |
25 <CODE>g = rbfbkp(net, x, z, n2, deltas)</CODE> takes a network data structure | |
26 <CODE>net</CODE> together with a matrix <CODE>x</CODE> of input vectors, a matrix | |
27 <CODE>z</CODE> of hidden unit activations, a matrix <CODE>n2</CODE> of the squared | |
28 distances between centres and inputs, and a matrix <CODE>deltas</CODE> of the | |
29 gradient of the error function with respect to the values of the | |
30 output units (i.e. the summed inputs to the output units, before the | |
31 activation function is applied). The return value is the gradient | |
32 <CODE>g</CODE> of the error function with respect to the network | |
33 weights. Each row of <CODE>x</CODE> corresponds to one input vector. | |
34 | |
35 <p>This function is provided so that the common backpropagation algorithm | |
36 can be used by RBF network models to compute | |
37 gradients for the output values (in <CODE>rbfderiv</CODE>) as well as standard error | |
38 functions. | |
39 | |
40 <p><h2> | |
41 See Also | |
42 </h2> | |
43 <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfderiv.htm">rbfderiv</a></CODE><hr> | |
44 <b>Pages:</b> | |
45 <a href="index.htm">Index</a> | |
46 <hr> | |
47 <p>Copyright (c) Ian T Nabney (1996-9) | |
48 | |
49 | |
50 </body> | |
51 </html> |