Adaptive preprocessing for on-line learning with adaptive resonance theory (ART) networks

Neural networks based on adaptive resonance theory (ART) are capable of on-line learning. However, a limiting factor in on-line processing has been the need to preprocess input patterns so that features fall in the range [0.0, 1.0], typically done with scale-factors that depend on the input range of each feature. This paper demonstrates a method by which the scaling of features becomes adaptive, eliminating the need to batch-process patterns before presenting them to the ART network. The resulting network implementation for on-line learning does not call for any knowledge of the feature signals, ranges or otherwise. A variety of implications of this scheme are analyzed.