Design probabilistic neural network
net = newpnn(P,T,spread)
Probabilistic neural networks (PNN) are a kind of radial basis network suitable for classification problems.
net = newpnn(P,T,spread)
takes two or three
arguments,
P 

T 

spread  Spread of radial basis functions (default = 0.1) 
and returns a new probabilistic neural network.
If spread
is near zero, the network acts
as a nearest neighbor classifier. As spread
becomes
larger, the designed network takes into account several nearby design
vectors.
Here a classification problem is defined with a set of inputs P
and
class indices Tc
.
P = [1 2 3 4 5 6 7]; Tc = [1 2 3 2 2 3 1];
The class indices are converted to target vectors, and a PNN is designed and tested.
T = ind2vec(Tc) net = newpnn(P,T); Y = sim(net,P) Yc = vec2ind(Y)
Wasserman, P.D., Advanced Methods in Neural Computing, New York, Van Nostrand Reinhold, 1993, pp. 35–55