原帖由 kevin19821 于 2007-10-28 14:54 发表
哪位高人给我讲解一下呀
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NEWRBE Design an exact radial basis network.
Synopsis
net = newrbe(P,T,SPREAD)
Description
Radial basis networks can be used to approximate functions.
NEWRBE very quickly designs a radial basis network with
zero error on the design vectors.
NEWRBE(P,T,SPREAD) takes two or three arguments,
P - RxQ matrix of Q input vectors.
T - SxQ matrix of Q target class vectors.
SPREAD - of radial basis functions, default = 1.0.
and returns a new exact radial basis network.
The larger that SPREAD, is the smoother the function approximation
will be. Too large a spread can cause numerical problems.
Examples
Here we design a radial basis network, given inputs P
and targets T.
P = [1 2 3];
T = [2.0 4.1 5.9];
net = newrbe(P,T);
Here the network is simulated for a new input.
P = 1.5;
Y = sim(net,P)
Algorithm
NEWRBE creates a two layer network. The first layer has RADBAS
neurons, and calculates its weighted inputs with DIST, and its
net input with NETPROD. The second layer has PURELIN neurons,
and calculates its weighted input with DOTPROD and its net inputs
with NETSUM. Both layer's have biases.
NEWRBE sets the first layer weights to P', and the first
layer biases are all set to 0.8326/SPREAD, resulting in
radial basis functions that cross 0.5 at weighted inputs
of +/- SPREAD.
The second layer weights IW{2,1} and biases b{2} are found by
simulating the first layer outputs A{1}, and then solving the
following linear expression:
[W{2,1} b{2}] * [A{1}; ones] = T
See also sim, newrb, newgrnn, newpnn.
Reference page in Help browser
doc newrbe |