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A Probabilistic Neural Network (PNN) is a Feedforward neural network , which was derived from Bayesian network[1] and a statistical algorithm called Kernel Fisher discriminant analysis[2]. It was introduced by D.F. Specht in the early 1990s. In a PNN, the operations are organized into a multilayered feedforward network with four layers:
- Input layer
- Pattern layer
- Summation layer
- Output layer
PNN often use in classification problems[3].When an Input is present, first layer computes the distance from the input vector to the training input vectors. It produce a vector where its elements indicate how close the input is to training input. The second layer sums the contribution for each class of inputs and produce it's net output as 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.
Advantages
There are several advantages and disadvantages using PNN instead of multilayer perceptron[4]
- PNN is much faster compare to multilayer perceptron networks.
- PNN are more accurate than multilayer perceptron networks.
- PNN are networks are relatively insensitive to outliers.
- PNN networks generate accurate predicted target probability scores.
- PNN approach Bayes optimal classification.
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disadvantages
- PNN are slower than multilayer perceptron networks at classifying new cases.
- PNN require more memory space to store the model