Adaptive resonance theory

It describes a number of artificial neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction.

The primary intuition behind the ART model is that object identification and recognition generally occur as a result of the interaction of 'top-down' observer expectations with 'bottom-up' sensory information.

The model postulates that 'top-down' expectations take the form of a memory template or prototype that is then compared with the actual features of an object as detected by the senses.

With fast learning, algebraic equations are used to calculate degree of weight adjustments to be made, and binary values are used.

ART 2-A[4] is a streamlined form of ART-2 with a drastically accelerated runtime, and with qualitative results being only rarely inferior to the full ART-2 implementation.

ART 3[5] builds on ART-2 by simulating rudimentary neurotransmitter regulation of synaptic activity by incorporating simulated sodium (Na+) and calcium (Ca2+) ion concentrations into the system's equations, which results in a more physiologically realistic means of partially inhibiting categories that trigger mismatch resets.

The coupling of the two Fuzzy ARTs has a unique stability that allows the system to converge rapidly towards a clear solution.

The effect can be reduced to some extent by using a slower learning rate, but is present regardless of the size of the input data set.

4 of [18]) Wasserman, Philip D. (1989), Neural computing: theory and practice, New York: Van Nostrand Reinhold, ISBN 0-442-20743-3