Design radial basis network
net = newrb(P,T,goal,spread,MN,DF)
Radial basis networks can be used to approximate functions. newrb
adds
neurons to the hidden layer of a radial basis network until it meets
the specified mean squared error goal.
net = newrb(P,T,goal,spread,MN,DF)
takes
two of these arguments,
P 

T 

goal  Mean squared error goal (default = 0.0) 
spread  Spread of radial basis functions (default = 1.0) 
MN  Maximum number of neurons (default is 
DF  Number of neurons to add between displays (default = 25) 
and returns a new radial basis network.
The larger spread
is, the smoother the function
approximation. Too large a spread means a lot of neurons are required
to fit a fastchanging function. Too small a spread means many neurons
are required to fit a smooth function, and the network might not generalize
well. Call newrb
with different spreads to find
the best value for a given problem.
Here you design a radial basis network, given inputs P
and
targets T
.
P = [1 2 3]; T = [2.0 4.1 5.9]; net = newrb(P,T);
The network is simulated for a new input.
P = 1.5; Y = sim(net,P)