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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 | R-by-Q matrix of Q input vectors |
T | S-by-Q matrix of Q target class vectors |
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