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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 | R-by-Q matrix of Q input vectors |
T | S-by-Q matrix of Q target class vectors |
goal | Mean squared error goal (default = 0.0) |
spread | Spread of radial basis functions (default = 1.0) |
MN | Maximum number of neurons (default is Q) |
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 fast-changing 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)