Herself’s Artificial Intelligence

Probabilistic Neural Networks

Probabilistic neural networks are forward feed networks built with three layers. They are derived from Bayes Decision Networks. They train quickly since the training is done in one pass of each training vector, rather than several. Probabilistic neural networks estimate the probability density function for each class based on the training samples.

The probabilistic neural network uses Parzen or a similar probability density function. This is calculated for each test vector. This is what is used in the dot product against the input vector as described below. Usually a spherical Gaussian basis function is used, although many other functions work equally well.

Vectors must be normalized prior to input into the network. There is an input unit for each dimension in the vector. The input layer is fully connected to the hidden layer. The hidden layer has a node for each classification. Each hidden node calculates the dot product of the input vector with a test vector subtracts 1 from it and divides the result by the standard deviation squared. The output layer has a node for each pattern classification. The sum for each hidden node is sent to the output layer and the highest values wins.

The Probabilistic neural network trains immediately but execution time is slow and it requires a large amount of space in memory. It really only works for classifying data. The training set must be a thorough representation of the data. Probabilistic neural networks handle data that has spikes and points outside the norm better than other neural nets.