Adaptive Resonance Theory (ART)

Adaptive Resonance Theory (ART) is a neural network model that helps systems learn to recognize patterns and categorize data efficiently. It balances stability (retaining learned patterns) with plasticity (adapting to new information) by creating and adjusting connections based on how well new input matches existing categories. When the input closely matches a stored pattern, the network reinforces that recognition; if not, it creates a new category. This process allows for continuous learning without forgetting earlier knowledge, making ART useful for tasks like pattern recognition, classification, and adaptive learning systems.