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

doi:10.1088/1674-1056/ad8148

中国物理B ›› 2024, Vol. 33 ›› Issue (12): 120502-120502.doi: 10.1088/1674-1056/ad8148

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

Qing-Qing Ma(马青青), An-Jiang Lu(陆安江)†, and Zhi Huang(黄智)

College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China

收稿日期:2024-08-13 修回日期:2024-09-24 接受日期:2024-09-30 发布日期:2024-11-12
通讯作者: An-Jiang Lu E-mail:ajlu@gzu.edu.cn
基金资助:
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)

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

Qing-Qing Ma(马青青), An-Jiang Lu(陆安江)†, and Zhi Huang(黄智)

College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China

Received:2024-08-13 Revised:2024-09-24 Accepted:2024-09-30 Published:2024-11-12
Contact: An-Jiang Lu E-mail:ajlu@gzu.edu.cn
Supported by:
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)

摘要/Abstract

摘要： 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.

关键词: multi-double scroll attractors, Hopfield neural network, pulse control

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.

Key words: multi-double scroll attractors, Hopfield neural network, pulse control

中图分类号: (Nonlinear dynamics and chaos)

05.45.-a

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[J]. 中国物理B, 2024, 33(12): 120502-120502.

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[J]. Chin. Phys. B, 2024, 33(12): 120502-120502.

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

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

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