Probabilistic Neural Network

PNN with an example. Source: Xoax.net 2009.

Explainingwith an example. Source: Xoax.net 2009.

Consider the task of classifying the letters O, X, and I. The characters can be in uppercase or lowercase. We consider two features: length and area of each character. Consequently, the training set will have 6 letters (O,o,X,x,I,i). Each training data point will be identified with a (length, area) value. For example, O(0.5,0.7), o(0.2,0.5), X(0.8,0.8), x(0.4,0.5), I(0.6,0.3) and i(0.3,0.2).

The input layer of the PNN will have two neurons, one for each feature, that is, one node for length and one for area.

We have three classes. Each class has two patterns in the pattern layer, one for uppercase and one for lowercase. For example, for class O there are two subtypes (O,o). In total, the pattern layer has six neurons.

The summation layer will calculate the average value for each pattern type of the pattern layer and output layer will pick the maximum value, thereby determining the suitable class O, X, I.

An advantage of PNN is that there is no back-propagation training. New pattern units can be added without additional time overhead, since no training is needed; it is automatic.