Coexisting and multiple scroll attractors in a Hopfield neural network with a controlled memristor

doi:10.1088/1674-1056/ad8148

Abstract A method of generating multi-double scroll attractors is proposed based on the memristor Hopfield neural network (HNN) under pulse control. First, the original hyperbolic-type memristor is added to the neural network mathematical model, and the influence of this memristor on the dynamic behavior of the new HNN is analyzed. The numerical results show that after adding the memristor, the abundant dynamic behaviors such as chaos coexistence, period coexistence and chaos period coexistence can be observed when the initial value of the system is changed. Then the logic pulse is added to the external memristor. It is found that the equilibrium point of the HNN can multiply and generate multi-double scroll attractors after the pulse stimulation. When the number of logical pulses is changed, the number of multi-double scroll attractors will also change, so that the pulse can control the generation of multi-double scroll attractors. Finally, the HNN circuit under pulsed stimulation was realized by circuit simulation, and the results verified the correctness of the numerical results.

Keywords: multi-double scroll attractors Hopfield neural network pulse control

Received: 13 August 2024
Revised: 24 September 2024
Accepted manuscript online: 30 September 2024

PACS:

05.45.-a

(Nonlinear dynamics and chaos)

Fund: This paper was supported by the Guizhou Province Natural Science Foundation (Qiankehe Fundamentals-ZK[2023]General-055) and Guizhou Province Science and Technology Support Plan Project (Qiankehe Fundamentals [2023] General-465).

Corresponding Authors: An-Jiang Lu
E-mail: ajlu@gzu.edu.cn

Cite this article:

Qing-Qing Ma(马青青), An-Jiang Lu(陆安江), and Zhi Huang(黄智) Coexisting and multiple scroll attractors in a Hopfield neural network with a controlled memristor 2024 Chin. Phys. B 33 120502

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Coexisting and multiple scroll attractors in a Hopfield neural network with a controlled memristor

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