Cellular neural networks with non‐linear and delay‐type template elements and non‐uniform grids

Tamás Roska, Leon O. Chua

Research output: Contribution to journalArticle

334 Citations (Scopus)

Abstract

The cellular neural network (CNN) paradigm is a powerful framework for analogue non‐linear processing arrays placed on a regular grid. In this paper we extend the current repertoire of CNN cloning template elements (atoms) by introducing additional non‐linear and delay‐type characteristics. In addition, architectures with non‐uniform processors and neighbourhoods (grid sizes) are introduced. With this generalization, several well‐known and powerful analogue array‐computing structures can be interpreted as special cases of the CNN. Moreover, we show that the CNN with these generalized cloning templates has a general programmable circuit structure (a prototype machine) with analogue macros and algorithms. the relations with the cellular automaton (CA) and the systolic array (SA) are analysed. Finally, some robust stability results and the state space structure of the dynamics are presented.

Original languageEnglish
Pages (from-to)469-481
Number of pages13
JournalInternational Journal of Circuit Theory and Applications
Volume20
Issue number5
DOIs
Publication statusPublished - Jan 1 1992

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Cellular neural networks with non‐linear and delay‐type template elements and non‐uniform grids'. Together they form a unique fingerprint.

  • Cite this