Memristor dynamics involved in cells communication for a 2D non-linear network

In this study, the authors consider a first step to apply memristor devices in a cellular non-linear network (CNN), where the advantages of the non-linearity, nanoscalability and memory effect could be taken into account to implement a 2D-CNN for signal and image processing – Memristors are used in...

Full description

Saved in:
Bibliographic Details
Published in:IET signal processing 2020-09, Vol.14 (7), p.427-434
Main Authors: Isah, Aliyu, Nguetcho, Aurelien Serge Tchakoutio, Binczak, Stéphane, Bilbault, Jean-Marie
Format: Article
Language:English
Subjects:
2D nonlinear network
adjacent cells
cells communication
cellular neural nets
cellular nonlinear network
CNN
mathematics computing
MATLAB software
memory effect
memristor devices
memristor dynamics
memristors
nanoscalability
nonlinear network
Research Article
SPICE
Citations: Items that this one cites
Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this study, the authors consider a first step to apply memristor devices in a cellular non-linear network (CNN), where the advantages of the non-linearity, nanoscalability and memory effect could be taken into account to implement a 2D-CNN for signal and image processing – Memristors are used in the coupling mode to connect adjacent cells serially. They drive the analytical model describing the transmission of information from one cell to another via memristor, whose description in the $\phi $ϕ–q plane is given, then improved to overcome the problem of discontinuities for some values of the charge $q\lpar t\rpar $q(t). The modified model is then chosen to be continued for all initial conditions and all parameters sets. Moreover, numerical simulations from SPICE and MATLAB software confirm the authors’ analytical predictions.
ISSN:1751-9675
1751-9683
1751-9683
DOI:10.1049/iet-spr.2020.0136