One-dimensional discrete-time binary cellular neural networks and some examples for signal processing

Hidenori Sato, Tetsuo Nishi, Norikazu Takahashi

Research output: Contribution to journalArticle

Abstract

This paper studies on the behavior of one-dimensional discrete-time binary cellular neural networks(abbreviated as a 1-D DBCNN). In general cellular neural networks(CNN) are characterized by the A- and the B-templates and the threshold value θ. Our final aim is to exhaustively examine the behavior of the 1-D DBCNN with arbitrary A-template, B-template and θ from the viewpoint of signal processing. In this paper we will give some partial solutions to this problem. We first show necessary and sufficient conditions for the system to be stable, and then give several numerical examples to illustrate the above results.

Original languageEnglish
Pages (from-to)93-98
Number of pages6
JournalResearch Reports on Information Science and Electrical Engineering of Kyushu University
Volume7
Issue number2
Publication statusPublished - Sep 2002
Externally publishedYes

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Cellular neural networks
Signal processing

Keywords

  • 1-D CNN
  • Signal processing
  • Stability condition

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

Cite this

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AB - This paper studies on the behavior of one-dimensional discrete-time binary cellular neural networks(abbreviated as a 1-D DBCNN). In general cellular neural networks(CNN) are characterized by the A- and the B-templates and the threshold value θ. Our final aim is to exhaustively examine the behavior of the 1-D DBCNN with arbitrary A-template, B-template and θ from the viewpoint of signal processing. In this paper we will give some partial solutions to this problem. We first show necessary and sufficient conditions for the system to be stable, and then give several numerical examples to illustrate the above results.

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