An implementation and evaluation of the ART1 neural network for pattern recognition

A key to solving the stability-plasticity dilemma is to add a feedback mechanism between the competitive and the input layer of a network. This feedback mechanism facilitates the learning of new information without destroying old information, automatic switching between stable and plastic modes, and stabilization of the encoding of the classes done by the nodes resulting from this approach we have a neural network architecture that is particularly suited for pattern-classification problems in real world environments. For industrial use, ART1 neural networks have the potential of becoming an important component in a variety of commercial and military systems. Efficient software emulations of these networks are adequate in many of today's low-end applications such as information retrieval or group technology; but for larger applications, special purpose hardware is required to achieve the expected performance requirements.<
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