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• W2154452018 abstract "Bidirectional associative memory (BAM) generalizes the associative memory (AM) to be capable of performing two-way recalling of pattern pairs. Asymmetric bidirectional associative memory (ABAM) is a variant of BAM relaxed with connection weight symmetry restriction and enjoys a much better performance than a conventional BAM structure. Higher-order associative memories (HOAMs) are reputed for their higher memory capacity than the first-order counterparts. The paper concerns the design of a second-order asymmetric bidirectional associative memory (SOABAM) with a maximal basin of attraction, whose extension to a HOABAM is possible and straightforward. First, a necessary and sufficient condition is derived for the connection weight matrix of SOABAM that can guarantee the recall of all prototype pattern pairs. A local training rule which is adaptive in the learning step size is formulated. Then derived is a theorem, designing a SOABAM further enlarging the quantities required to meet the complete recall theorem will enhance the capability of evolving a noisy pattern to converge to its association pattern vector without error. Based on this theorem, our algorithm is also modified to ensure each training pattern is stored with a basin of attraction as large as possible." @default.
• W2154452018 created "2016-06-24" @default.
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• W2154452018 date "2003-07-01" @default.
• W2154452018 modified "2023-09-23" @default.
• W2154452018 title "Second-order asymmetric BAM design with a maximal basin of attraction" @default.
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• W2154452018 doi "https://doi.org/10.1109/tsmca.2003.811505" @default.
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