Artificial Neural Networks/ART Learning

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ART Learning[edit]

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.