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* * * * * * * * * * * * * * * * Neural Networks Lecture 19: Adaptive Resonance Theory Neural Networks Lecture 19: Adaptive Resonance Theory Adaptive Resonance Theory (ART) networks perform completely unsupervised learning. Their competitive learning algorithm is similar to the first (unsupervised) phase of CPN learning. However, ART networks are able to grow additional neurons if a new input cannot be categorized appropriately with the existing neurons. A vigilance parameter ? determines the tolerance of this matching process. A greater value of ? leads to more, smaller clusters (= input samples associated with the same winner neuron). Adaptive Resonance Theory (ART) November 23, 2010 * Neural Networks Lecture 19: Adaptive Resonance Theory ART networks consist of an input layer and an output layer. We will only discuss ART-1 networks, which receive binary input vectors. Bottom-up weights are used to determine output-layer candidates that may best match the current input. Top-down weights represent the “prototype” for the cluster defined by each output neuron. A close match between input and prototype is necessary for categorizing the input. Finding this match can require multiple signal exchanges between the two layers in both directions until “resonance” is established or a new neuron is added. Adaptive Resonance Theory (ART) November 23, 2010 * Neural Networks Lecture 19: Adaptive Resonance Theory ART networks tackle the stability-plasticity dilemma: Plasticity: They can always adapt to unknown inputs (by creating a new cluster with a new weight vector) if the given input cannot be classified by existing clusters. Stability: Existing clusters are not deleted by the introduction of new inputs (new clusters w