📄 rbffwd.htm
字号:
<html><head><title>Netlab Reference Manual rbffwd</title></head><body><H1> rbffwd</H1><h2>Purpose</h2>Forward propagation through RBF network with linear outputs.<p><h2>Synopsis</h2><PRE>a = rbffwd(net, x)function [a, z, n2] = rbffwd(net, x)</PRE><p><h2>Description</h2><CODE>a = rbffwd(net, x)</CODE> takes a network data structure<CODE>net</CODE> and a matrix <CODE>x</CODE> of inputvectors and forward propagates the inputs through the network to generatea matrix <CODE>a</CODE> of output vectors. Each row of <CODE>x</CODE> corresponds to oneinput vector and each row of <CODE>a</CODE> contains the corresponding output vector.The activation function that is used is determined by <CODE>net.actfn</CODE>.<p><CODE>[a, z, n2] = rbffwd(net, x)</CODE> also generates a matrix <CODE>z</CODE> ofthe hidden unit activations where each row corresponds to one pattern.These hidden unit activations represent the <CODE>design matrix</CODE> forthe RBF. The matrix <CODE>n2</CODE> is the squared distances between eachbasis function centre and each pattern in which each row correspondsto a data point.<p><h2>Examples</h2><PRE>[a, z] = rbffwd(net, x);<p>temp = pinv([z ones(size(x, 1), 1)]) * t;net.w2 = temp(1: nd(2), :);net.b2 = temp(size(x, nd(2)) + 1, :);</PRE>Here <CODE>x</CODE> is the input data, <CODE>t</CODE> are the target values, and we use thepseudo-inverse to find the output weights and biases.<p><h2>See Also</h2><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><b>Pages:</b><a href="index.htm">Index</a><hr><p>Copyright (c) Ian T Nabney (1996-9)</body></html>⌨️ 快捷键说明
复制代码
Ctrl + C
搜索代码
Ctrl + F
全屏模式
F11
切换主题
Ctrl + Shift + D
显示快捷键
?
增大字号
Ctrl + =
减小字号
Ctrl + -