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

T. Roska, Leon O. Chua

Research output: Contribution to journalArticle

326 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 analyzed. 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
Publication statusPublished - Sep 1992

Fingerprint

Non-uniform Grid
Cellular neural networks
Cellular Networks
Template
Neural Networks
Cloning
Analogue
Grid
Systolic Array
Array processing
Systolic arrays
Cellular automata
Robust Stability
Cellular Automata
Macros
State Space
Paradigm
Prototype
Atoms
Networks (circuits)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Cellular neural networks with non-linear and delay-type template elements and non-uniform grids. / Roska, T.; Chua, Leon O.

In: International Journal of Circuit Theory and Applications, Vol. 20, No. 5, 09.1992, p. 469-481.

Research output: Contribution to journalArticle

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