newpnn (Neural Network Toolbox)

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newpnn

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Design a probabilistic neural network

Syntax

net = newpnn(P,T,spread)

Description

Probabilistic neural networks are a kind of radial basis network suitable for classification problems.

net = newpnn(P,T,spread)takes two or three arguments,

P - R x Q matrix of Q input vectors.

T - S x 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 will act as a nearest neighbor classifier. As spread becomes larger the designed network will take into account several nearby design vectors.

Examples

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];

Here 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)

Algorithm

newpnn creates a two layer network. The first layer has radbas neurons, and calculates its weighted inputs with dist, and its net input with netprod. The second layer has compet neurons, and calculates its weighted input with dotprod and its net inputs with netsum. Only the first layer has biases.

newpnn sets the first layer weights to P', and the first layer biases are all set to 0.8326/spread resulting in radial basis functions that cross 0.5 at weighted inputs of +/- spread. The second layer weights W2 are set to T.