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A quick learning rule to expand stable attraction basins in autoassociative neural networks

In this paper, a quick repeated learning rule, which is based on the Hebb rule and the Hamming distance distribution of the pattern set to be learned, is studied. With the help of the proposed learning rule, not only can the learned patterns be addressable, but an attraction basin with a predetermined radius is established for each attractor.