Neural networks and fuzzy logic resonance theory art neural networks and fuzzy logic valluru

C++ Neural Networks and Fuzzy Logic:Adaptive Resonance Theory (ART)

Grossberg’s Adaptive Resonance Theory, developed further by Grossberg and Carpenter, is for the categorization of patterns using the competitive learning paradigm. It introduces a gain control and a reset to make certain that learned categories are retained even while new categories are learned and thereby addresses the plasticity–stability dilemma.

Adaptive Resonance Theory makes much use of a competitive learning paradigm. A criterion is developed to facilitate the occurrence of winner-take-all phenomenon. A single node with the largest value for the set criterion is declared the winner within its layer, and it is said to classify a pattern class. If there is a tie for the winning neuron in a layer, then an arbitrary rule, such as the first of them in a serial order, can be taken as the winner.

C++ Neural Networks and Fuzzy Logic:Adaptive Resonance Theory (ART)

photograph matches the likeness of the subject to a greater degree when the granularity is higher, the pattern match gets finer when the vigilance parameter is closer to 1.

Figure 10.1 simplified diagram of the neural network for an ART1 model.

Processing in ART1