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Design and Analysis of High-Capacity Associative Memories Based on a Class of Discrete-Time Recurrent Neural Networks
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
This paper presents a design method for synthesizing associative memories based on discrete-time recurrent neural networks. The proposed procedure enables both hetero-and autoassociative memories to be synthesized with high storage capacity and assured global asymptotic stability. The stored patterns are retrieved by feeding probes via external inputs rather than initial conditions. As typical representatives, discrete-time cellular neural networks (CNNs) designed with space-invariant cloningdoi:10.1109/tsmcb.2008.927717 pmid:19022724 fatcat:vdy2jdv7xrednfx5jylpolfvq4