A non-local structural derivative model for memristor
Creators
- 1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Center for Numerical Simulation Software in Engineering and Sciences, College of Mechanics and Materials, Hohai University, Nanjing 210098 (China)
- 2. National Engineering Research Center for Intelligent Electrical Vehicle Power System, School of Electromechanical Engineering, Qingdao University, Qingdao, Shandong, 266071 (China)
Description
The memristor is of great application and significance in the integrated circuit design, the realization of large-capacity non-volatile memories and the neuromorphic systems. This paper firstly proposes the non-local structural derivative memristor model with two-degree-of-freedom increased to portray the memory effect of memristor. Actually, the developed is a more generalized model that will be reduced to the classical one when the fractal characteristic index α = 1. The proposed model is more flexible than the classical ideal memory model and Riemann Liouville memristor model under the same conditions. In addition, the memory effect described by the present scheme could be adjusted by the position parameter δ. This work provides a new methodology not only to describe the memory effect of the memristor, but also to easily portray the memristor with ultra-weak memory.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.chaos.2019.05.040Additional details
Identifiers
- DOI
- 10.1016/j.chaos.2019.05.040;
- PII
- S096007791930205X;
Publishing Information
- Journal Title
- Chaos, Solitons and Fractals
- Journal Volume
- 126
- Journal Page Range
- p. 169-177
- ISSN
- 0960-0779
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 54120671
- Subject category
- S97: MATHEMATICAL METHODS AND COMPUTING;
- Descriptors DEI
- DEGREES OF FREEDOM; DESIGN; FRACTALS; INTEGRATED CIRCUITS
- Descriptors DEC
- ELECTRONIC CIRCUITS; MICROELECTRONIC CIRCUITS
Optional Information
- Copyright
- Copyright (c) 2019 Elsevier Ltd. All rights reserved.