Discrete‐time cellular neural networks

1 September 1992

journal article
research article
Published by Wiley in International Journal of Circuit Theory and Applications

Vol. 20 (5) , 453-467
https://doi.org/10.1002/cta.4490200503

Abstract

A network structure called a discrete‐time cellular neural network is introduced. It is derived from cellular neural networks and feedback threshold networks. the architecture is discussed and its advantages are presented. Convergence is proved for a large class of templates and applications are given for the following image‐processing tasks: linear thresholding, connected component detection, hole filling, concentric contouring, increasing and decreasing objects step by step, searching for objects with minimal distance, and oscillation.

Keywords

This publication has 13 references indexed in Scilit:

Cellular neural networks: Theory and circuit design
International Journal of Circuit Theory and Applications, 1992
On the convergence properties of the Hopfield model
Proceedings of the IEEE, 1990
CNN cloning template: connected component detector
IEEE Transactions on Circuits and Systems, 1990
CNN cloning template: hole-filler
IEEE Transactions on Circuits and Systems, 1990
Stability of a class of nonreciprocal cellular neural networks
IEEE Transactions on Circuits and Systems, 1990
CNN cloning template: shadow detector
IEEE Transactions on Circuits and Systems, 1990
Cellular neural networks: applications
IEEE Transactions on Circuits and Systems, 1988
Cellular neural networks: theory
IEEE Transactions on Circuits and Systems, 1988
Decreasing energy functions as a tool for studying threshold networks
Discrete Applied Mathematics, 1985
Fixed Point Behavior of Threshold Functions on a Finite Set
SIAM Journal on Algebraic Discrete Methods, 1982