Artificial Neural Networks/ART Learning

ART LearningEdit

Adaptive Resonance Theory (ART) learning algorithms compare the weight vector, known as the prototype, to the current input vector to produce a distance, r. The distance is compared to a specified scalar, the vigilance parameter p. All output nodes start off in the uncommitted state. When a new input sequence is detected that does not resonate with any committed nodes, an uncommitted node is committed, and it’s prototype vector is set to the current input vector.

Last modified on 30 October 2007, at 13:34