A self-organizing neural network for supervised learning, recognition, and prediction

摘要

Fuzzy ARTMAP, one of a rapidly growing family of attentive self-organizing learning, hypothesis testing, and prediction systems that have evolved from the biological theory of cognitive information processing of which ART forms an important part is discussed. It is shown that this architecture is capable of fast but stable online recognition learning, hypothesis testing and adaptive naming in response to an arbitrary stream of analog or binary input patterns. The fuzzy ARTMAP neural network combines a unique set of computational abilities that are needed to function autonomously in a changing world and that alternative models have not yet achieved. In particular, fuzzy ARTMAP can autonomously learn, recognize, and make predictions about rare events, large nonstationary databases, morphologically variable types of events, and many-to-one and one-to-many relationships. The system's fast learning of rare events and error-based learning and alternatives are described, and uses for ART systems and the development of unsupervised ART systems are reviewed.