Memristor Cellular Nonlinear Networks

Author:

Slavova Angela1,Ignatov Ventsislav2

Affiliation:

1. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria

2. Laboratory of Engineering Mathematics, Ruse University “Angel Kanchev”, 7017 Ruse, Bulgaria

Abstract

This paper presents a review of the theory and applications of memristor cellular nonlinear networks. By mapping the physical processes to the memristive framework, all resistive switching devices can be modeled. The idea is to find a state variable that presents with high accuracy the important features of the system, its dynamics, and time evolution in response to the inputs of the memristor. In order to develop a new design of memristor-based cellular nonlinear networks (MCNN), new circuital and mathematical memristor models need to be introduced. In this way, implementation into new software packages for computer-aided integrated circuit realization can be achieved. Another challenging problem is studying the complex behavior of MCNN models by means of local activity theory and generalizing it for various test cases. An application of the hardware implementation of these models can be found in nanostructures.

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Reference74 articles.

1. Cellular neural networks: Theory;Chua;IEEE Trans. Circuits Syst.,1988

2. Tetzlaff, R. (2014). Memristor and Memristive Systems, Springer.

3. Loroch, D.M. (2016). Study and Analysis of CNN with Real Memristor Devices. [Master’s Thesis].

4. Memristor-Based Cellular Nonlinear/Neural Networks: Design, Analysis, and Applications;Duan;IEEE Trans. Neural Netw. Learn. Syst.,2015

5. Chua, L.O. (1969). Introduction to Nonlinear Network Theory, McGraw-Hill. McGraw-Hill Series in Electronic Systems.

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