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An architecture and learning methods for deep neural networks that increase a number of layers and adjust their synaptic weights in an online mode are proposed in the article. The system's architecture is based on nodes of a special type (extended neo-fuzzy neurons) which possess enhanced approximating properties. A main feature of the proposed network is a learning process for each node that is performed...
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