A novel memristive synapse-coupled ring neural network with countless attractors and its application

Sen Zhang, Yongxin Li, Daorong Lu and Chunbiao Li

Chaos, Solitons & Fractals, 2024, vol. 184, issue C

Abstract: This paper presents a novel memristive synapse-coupled ring neural network (MSCRNN) through introducing a nonvolatile memristor into a three-neuron Hopfield neural network connected by a unidirectional ring topology. Complex dynamics relying on control parameters and initial states is thoroughly explored using numerical analysis techniques. Numerical analyses show that the MSCRNN not only exhibits bistability, tristability, but also in particular evolves an intriguing phenomenon known as homogeneous multistability, characterized by the emergence of an infinite number of homogeneous coexisting attractors triggered by the memristor initial states. In addition, a hardware test platform based on the CH32 microcontroller is built to experimentally validate these numerical findings. Finally, a new pseudorandom number generator is developed taking advantage of memristor initial-regulated chaotic sequences derived from the MSCRNN. Performance analysis outcomes indicate that these chaotic sequences possess the capability to generate pseudorandom numbers demonstrating exceptional randomness, rendering them highly advantageous for utilization in various chaos-based engineering applications.

Keywords: Memristor; Ring neural network; Initial state; Homogeneous multistability; Hardware experiment (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077924006088
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:184:y:2024:i:c:s0960077924006088

DOI: 10.1016/j.chaos.2024.115056

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().