A synthesis procedure for associative memories based on space-varying cellular neural networks

J. Park, H. Y. Kim, Y. Park, S. W. Lee

Research output: Contribution to journalArticle

37 Citations (Scopus)

Abstract

In this paper, we consider the problem of realizing associative memories via space-varying CNNs (cellular neural networks). Based on some known results and a newly derived theorem for the CNN model, we propose a synthesis procedure for obtaining a space-varying CNN that can store given bipolar vectors with certain desirable properties. The major part of our synthesis procedure consists of solving generalized eigenvalue problems and/or linear matrix inequality problems, which can be efficiently solved by recently developed interior point methods. The validity of the proposed approach is illustrated by a design example.

Original languageEnglish
Pages (from-to)107-113
Number of pages7
JournalNeural Networks
Volume14
Issue number1
DOIs
Publication statusPublished - 2001 Jan

Keywords

  • Associative memory
  • Cellular neural network
  • Generalized eigenvalue problem
  • Linear matrix inequality problem

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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