中国物理B ›› 2026, Vol. 35 ›› Issue (1): 10502-010502.doi: 10.1088/1674-1056/ae1dec

• • 上一篇    下一篇

Dynamic analysis and DNA coding-based image encryption of memristor synapse-coupled hyperchaotic IN-HNN network

Shuang Zhao(赵双)1,2, Yunzhen Zhang(张云贞)3, Xiangjun Chen(陈湘军)1, Bin Gao(高彬)4, and Chengjie Chen(陈成杰)4,†   

  1. 1 School of Software and Big Data, Changzhou College of Information Technology, Changzhou 213164, China;
    2 Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia;
    3 School of Information Engineering, Xuchang University, Xuchang 461000, China;
    4 School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
  • 收稿日期:2025-09-10 修回日期:2025-11-07 接受日期:2025-11-11 发布日期:2025-12-30
  • 通讯作者: Chengjie Chen E-mail:chengjiechen@jsut.edu.cn
  • 基金资助:
    Project supported by the Training Plan of Young Backbone Teachers in Universities of Henan Province (Grant No. 2023GGJS142), the Key Scientific Research of Colleges and Universities in Henan Province, China (Grant No. 25A120009), Changzhou Leading Innovative Talent Introduction and Cultivation Project (Grant No. CQ20240102), and Changzhou Applied Basic Research Program (Grant No. CJ20253065).

Dynamic analysis and DNA coding-based image encryption of memristor synapse-coupled hyperchaotic IN-HNN network

Shuang Zhao(赵双)1,2, Yunzhen Zhang(张云贞)3, Xiangjun Chen(陈湘军)1, Bin Gao(高彬)4, and Chengjie Chen(陈成杰)4,†   

  1. 1 School of Software and Big Data, Changzhou College of Information Technology, Changzhou 213164, China;
    2 Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia;
    3 School of Information Engineering, Xuchang University, Xuchang 461000, China;
    4 School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
  • Received:2025-09-10 Revised:2025-11-07 Accepted:2025-11-11 Published:2025-12-30
  • Contact: Chengjie Chen E-mail:chengjiechen@jsut.edu.cn
  • Supported by:
    Project supported by the Training Plan of Young Backbone Teachers in Universities of Henan Province (Grant No. 2023GGJS142), the Key Scientific Research of Colleges and Universities in Henan Province, China (Grant No. 25A120009), Changzhou Leading Innovative Talent Introduction and Cultivation Project (Grant No. CQ20240102), and Changzhou Applied Basic Research Program (Grant No. CJ20253065).

PDF (PC)

3

赞

摘要： The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods. To this end, the paper proposes a novel high-dimensional memristor synapse-coupled hyperchaotic neural network by using the designed memristor as the synapse to connect an inertial neuron (IN) and a Hopfield neural network (HNN). By using numerical tools including bifurcation plots, phase plots, and basins of attraction, it is found that the dynamics of this system are closely related to the memristor coupling strength, self-connection synaptic weights, and inter-connection synaptic weights, and it can exhibit excellent hyperchaotic behaviors and coexisting multi-stable patterns. Through PSIM circuit simulations, the complex dynamics of the coupled IN-HNN system are verified. Furthermore, a DNA-encoded encryption algorithm is given, which utilizes generated hyperchaotic sequences to achieve encoding, operation, and decoding of DNA. The results show that this algorithm possesses strong robustness against statistical attacks, differential attacks, and noise interference, and can effectively resist known/selected plaintext attacks. This work will provide new ideas for the modeling of large-scale brain-like neural networks and high-security image encryption.

关键词: inertial neuron (IN), Hopfield neural network (HNN), memristor synapse, hyperchaotic attractor, image encryption

Abstract: The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods. To this end, the paper proposes a novel high-dimensional memristor synapse-coupled hyperchaotic neural network by using the designed memristor as the synapse to connect an inertial neuron (IN) and a Hopfield neural network (HNN). By using numerical tools including bifurcation plots, phase plots, and basins of attraction, it is found that the dynamics of this system are closely related to the memristor coupling strength, self-connection synaptic weights, and inter-connection synaptic weights, and it can exhibit excellent hyperchaotic behaviors and coexisting multi-stable patterns. Through PSIM circuit simulations, the complex dynamics of the coupled IN-HNN system are verified. Furthermore, a DNA-encoded encryption algorithm is given, which utilizes generated hyperchaotic sequences to achieve encoding, operation, and decoding of DNA. The results show that this algorithm possesses strong robustness against statistical attacks, differential attacks, and noise interference, and can effectively resist known/selected plaintext attacks. This work will provide new ideas for the modeling of large-scale brain-like neural networks and high-security image encryption.

Key words: inertial neuron (IN), Hopfield neural network (HNN), memristor synapse, hyperchaotic attractor, image encryption

中图分类号:  (Nonlinear dynamics and chaos)

  • 05.45.-a
05.45.Pq (Numerical simulations of chaotic systems)