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Hopfield learning rule with high capacity storage of time-correlated patterns

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)1803-1804
Number of pages2
JournalElectronics Letters
Volume33
Issue number21
DOIs
Publication statusPublished - 9 Oct 1997

Abstract

A new local and incremental learning rule is examined for its ability to store patterns from a time series in an attractor neural network. This learning rule has a higher capacity than the Hebb rule, and suffers significantly less capacity loss as the correlation between patterns increases.

    Research areas

  • Hopfield neural networks, MEMORY

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