Design generalized regression neural network
net = newgrnn(P,T,spread)
Generalized regression neural networks (
are a kind of radial basis network that is often used for function
grnns can be designed very quickly.
net = newgrnn(P,T,spread) takes three inputs,
Spread of radial basis functions (default = 1.0)
and returns a new generalized regression neural network.
The larger the
spread, the smoother the function
approximation. To fit data very closely, use a
than the typical distance between input vectors. To fit the data more
smoothly, use a larger
newgrnn creates a two-layer network. The
first layer has
radbas neurons, and calculates
weighted inputs with
dist and net input with
The second layer has
purelin neurons, calculates
weighted input with
normprod, and net inputs with
Only the first layer has biases.
newgrnn sets the first layer weights to
and the first layer biases are all set to
resulting in radial basis functions that cross 0.5 at weighted inputs
spread. The second layer weights
Here you design a radial basis network, given inputs
P = [1 2 3]; T = [2.0 4.1 5.9]; net = newgrnn(P,T);
The network is simulated for a new input.
P = 1.5; Y = sim(net,P)
Wasserman, P.D., Advanced Methods in Neural Computing, New York, Van Nostrand Reinhold, 1993, pp. 155–61