Associative memories based on continuous-time cellular neural networks designed using space-invariant cloning templates

Zhigang Zeng, Jun Wang

Research output: Contribution to journalArticlepeer-review

75 Citations (Scopus)

Abstract

Associative memories are brain-style devices designed to store a set of patterns as stable equilibria such that the stored patterns can be reliably retrieved with the initial probes containing sufficient information about the patterns. This paper presents a new design procedure for synthesizing associative memories based on continuous-time cellular neural networks with time delays characterized by input and output matrices obtained using two-dimensional space-invariant cloning templates. The design procedure enables hetero-associative or auto-associative memories to be synthesized by solving a set of linear inequalities with few design parameters and retrieval probes feeding from external inputs instead of initial states. The designed associative memories are robust in terms of design parameter selection. In addition, the hosting cellular neural networks are guaranteed to be globally exponentially stable. Simulation and experimental results of illustrative examples and Monte Carlo tests demonstrate the applicability and superiority of the methodology.

Original languageEnglish
Pages (from-to)651-657
Number of pages7
JournalNeural Networks
Volume22
Issue number5-6
DOIs
Publication statusPublished - Jul 2009
Externally publishedYes

Keywords

  • Associative memories
  • Cellular neural networks
  • Global exponential stability
  • Monte Carlo simulations
  • Two-dimensional space-invariant cloning templates

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