Stable patterns realized by a class of one-dimensional two-layer CNNs

Norikazu Takahashi, Makoto Nagayoshi, Susumu Kawabata, Tetsuo Nishi

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Stable patterns that can be realized by a class of 1-D two-layer cellular neural networks (CNNs) are studied in this paper. We first introduce the notions of potentially stable pattern, potentially stable local pattern, and local pattern set. We then show that all of 256 possible sets can be realized as the local pattern set of the two-layer CNN, while only 59 sets can be realized as the local pattern set of the single-layer CNN. We also propose a simple way to optimize the template values of the CNN, which is formulated as a set of linear programming problems, and present the obtained values for all of 256 sets.

Original languageEnglish
Pages (from-to)3607-3620
Number of pages14
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Volume55
Issue number11
DOIs
Publication statusPublished - Dec 1 2008
Externally publishedYes

Keywords

  • Cellular neural networks (CNNs)
  • Hidden layer
  • Stable patterns
  • Template optimization
  • Two layers

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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