Unsupervised learning with ART on the CNAPS

The most popular types of neural network training, such as back-propagation, involve a teacher, or supervisor, that provides a desired output for each input pattern. Supervised algorithms then attempt to minimize differences between the network outputs and the desired outputs. Unsupervised training algorithms, on the other hand, attempt to categorize the data without any external guide to the class of a given training pattern. Such networks can be valuable for analyzing data to search without bias for unknown classes, trends and relationships. Adaptive Resonance Theory (ART) algorithms, in particular, are a very popular form of unsupervised training. ART networks can not only learn to differentiate data into categories, but they easily learn new classes without destroying prior learning (solving the so called “stability-plasticity” dilemma). Here we report on the implementation of ART networks on the Adaptive Solutions CNAPS parallel processor system to obtain very fast unsupervised learning. We aim to use these capabilities for applications in “data mining” of large data sets such as those from high energy physics, remote sensing, etc.