Probabilistic Neural Network (PNN) is kind of feedforward neural network. In the PNN algorithm, the parent probability distribution function (PDF) of each class is approximated by a Parzen window and a nonparametric function. Then, using PDF of each class, the class probability of a new input data is estimated and Bayes’ rule is then employed to allocate the class with highest posterior probability to new input data. By this method, the probability of misclassification is minimized. This type of ANN was derived from the Bayesian network and a statistical algorithm called Kernel Fisher discriminant analysis. In a PNN, the operations are organized into a multilayered feedforward network with four layers: an input layer, hidden layer, pattern layer/summation layer, output layer. There are multiple applications based on PNN, for example, probabilistic neural networks in modeling structural deterioration of stormwater pipes.
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