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Probabilistic neural networks can be used for classification problems. When an input is presented, the first layer
computes distances from the input vector to the training input vectors and produces a
vector whose elements indicate how close the input is to a training input. The second
layer sums these contributions for each class of inputs to produce as its net output
a vector of probabilities. Finally, a *compete* transfer function
on the output of the second layer picks the maximum of these probabilities, and
produces a 1 for that class and a 0 for the other classes. The architecture for this
system is shown below.

It is assumed that there are *Q* input vector/target vector
pairs. Each target vector has `K`

elements. One of these
elements is 1 and the rest are 0. Thus, each input vector is associated with one
of `K`

classes.

The first-layer input weights, IW^{1,1}
(`net.IW{1,1}`

), are set to the transpose of the matrix
formed from the *Q* training pairs, **P**`'`

. When an input is presented, the
`||`

`dist`

`||`

box produces a vector whose elements indicate how close
the input is to the vectors of the training set. These elements are multiplied,
element by element, by the bias and sent to the `radbas`

transfer function. An
input vector close to a training vector is represented by a number close to 1 in
the output vector **a**^{1}. If an input is close to several
training vectors of a single class, it is represented by several elements of
**a**^{1} that are
close to 1.

The second-layer weights, LW^{1,2}
(`net.LW{2,1}`

), are set to the matrix **T** of target vectors. Each vector has a 1 only in the
row associated with that particular class of input, and 0s elsewhere. (Use
function `ind2vec`

to create the proper
vectors.) The multiplication **Ta**^{1} sums the elements of **a**^{1} due to each of the
`K`

input classes. Finally, the second-layer transfer
function, `compet`

, produces a 1
corresponding to the largest element of **n**^{2}, and 0s elsewhere. Thus, the
network classifies the input vector into a specific `K`

class
because that class has the maximum probability of being correct.

You can use the function `newpnn`

to create a PNN. For instance, suppose that seven input vectors and their
corresponding targets are

P = [0 0;1 1;0 3;1 4;3 1;4 1;4 3]'

which yields

P = 0 1 0 1 3 4 4 0 1 3 4 1 1 3 Tc = [1 1 2 2 3 3 3]

which yields

Tc = 1 1 2 2 3 3 3

You need a target matrix with 1s in the right places. You can get it with the
function `ind2vec`

. It gives a matrix with
0s except at the correct spots. So execute

T = ind2vec(Tc)

which gives

T = (1,1) 1 (1,2) 1 (2,3) 1 (2,4) 1 (3,5) 1 (3,6) 1 (3,7) 1

Now you can create a network and simulate it, using the input
`P`

to make sure that it does produce the correct
classifications. Use the function `vec2ind`

to convert the output
`Y`

into a row `Yc`

to make the
classifications clear.

net = newpnn(P,T); Y = sim(net,P); Yc = vec2ind(Y)

This produces

Yc = 1 1 2 2 3 3 3

You might try classifying vectors other than those that were used to design the
network. Try to classify the vectors shown below in
`P2`

.

P2 = [1 4;0 1;5 2]' P2 = 1 0 5 4 1 2

Can you guess how these vectors will be classified? If you run the simulation and plot the vectors as before, you get

Yc = 2 1 3

These results look good, for these test vectors were quite close to members of classes 2, 1, and 3, respectively. The network has managed to generalize its operation to properly classify vectors other than those used to design the network.

You might want to try PNN Classification. It shows how to design a PNN, and how the network can successfully classify a vector not used in the design.