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

Makoto Nagayoshi, Norikazu Takahashi, Tetsuo Nishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents some properties of stable patterns that can be realized by a certain type of one-dimensional two-layer cellular neural networks (CNNs). We first introduce a notion of admissible local pattern (ALP) set. All the stable patterns of a CNN can be completely determined by the ALP set. We next show that all of 256 possible ALP sets can be realized by two-layer CNNs, while only 59 can be realized by single-layer CNNs. This means two-layer CNNs have a much higher potential for signal processing than single-layer CNNs.

Original languageEnglish
Title of host publicationMidwest Symposium on Circuits and Systems
Volume1
Publication statusPublished - 2004
Externally publishedYes
EventThe 2004 47th Midwest Symposium on Circuits and Systems - Conference Proceedings - Hiroshima, Japan
Duration: Jul 25 2004Jul 28 2004

Other

OtherThe 2004 47th Midwest Symposium on Circuits and Systems - Conference Proceedings
CountryJapan
CityHiroshima
Period7/25/047/28/04

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

Cite this

Nagayoshi, M., Takahashi, N., & Nishi, T. (2004). On stable patterns realized by a class of one-dimensional two-layer CNNs. In Midwest Symposium on Circuits and Systems (Vol. 1)