PROPERTIES OF LEARNING RELATED TO PATTERN DIVERSITY IN ART1

In this paper we consider a special class of the ART1 neural network. It is shown that if this network is repeatedly presented with an arbitrary list of binary input patterns, learning self-stabilizes in at most m list presentations, where m corresponds to the number of patterns of distinct size in the input list. Other useful properties of the ART1 network, associated with the learning of an arbitrary list of binary input patterns, are also examined. These properties reveal some of the "good" characteristics of the ART1 network when it is used as a tool for the learning of recognition categories.