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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 `demopnn1`. It shows
how to design a PNN, and how the network can successfully classify
a vector not used in the design.

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