Design and analysis of high-capacity associative memories based on a class of discrete-time recurrent neural networks

Zhigang Zeng, Jun Wang

Research output: Contribution to journalArticlepeer-review

124 Citations (Scopus)

Abstract

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 cloning templates are examined in detail. In particular, it is shown that procedure herein can determine the input matrix of any CNN based on a space-invariant cloning template which involves only a few design parameters. Two specific examples and many experimental results are included to demonstrate the characteristics and performance of the designed associative memories.

Original languageEnglish
Pages (from-to)1525-1536
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume38
Issue number6
DOIs
Publication statusPublished - 2008
Externally publishedYes

Keywords

  • Autoassociative memory
  • Cellular neural networks (CNNs)
  • Cloning template
  • Heteroassociative memory

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