Classification characteristics of SOM and ART2
J. J. Aleshunas, Daniel C. St. Clair, W. E. Bond · 1994
and therefore adapts the resulting network to these patterns.Artificial neural network algorithms were originally designed to model human neural activities.They attempt to recreate the processes involved in such activities as learning, short term memory, and long term memory.Two widely used unsupervised artificial neural network algorithms are the Self-Organizing Map (SOM) and Adaptive Resonance Theory (ART2).Each was designed to simulate a particular biological neural activity.Both can be used as unsupervised data classifiers.This paper compares performance characteristics of two unsupervised artificial neural network architectures; the SOM and the ART2 networks.The primary factors analyzed were classification accuracy, sensitivity to data noise, and sensitivity of the algorithm control parameters.Guidelines are developed for algorithm selection.