Design radial basis network
net = newrb(P,T,goal,spread,MN,DF)
Radial basis networks can be used to approximate functions.
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,
Mean squared error goal (default = 0.0)
Spread of radial basis functions (default = 1.0)
Maximum number of neurons (default is
Number of neurons to add between displays (default = 25)
and returns a new radial basis network.
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
newrb with different spreads to find
the best value for a given problem.
Here you design a radial basis network, given inputs
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)
newrb creates a two-layer network. The first
radbas neurons, and calculates its weighted
dist and its net input with
The second layer has
purelin neurons, and calculates
its weighted input with
dotprod and its net inputs
netsum. Both layers have biases.
radbas layer has no neurons.
The following steps are repeated until the network's mean squared
error falls below
The network is simulated.
The input vector with the greatest error is found.
is added with weights equal to that vector.
weights are redesigned to minimize error.