Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

doi:10.1088/1674-1056/ad03dd

中国物理B ›› 2024, Vol. 33 ›› Issue (4): 40506-040506.doi: 10.1088/1674-1056/ad03dd

Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

Xinwei Zhou(周新卫)¹, Donghua Jiang(蒋东华)^2,†, Jean De Dieu Nkapkop³, Musheer Ahmad⁴, Jules Tagne Fossi⁵, Nestor Tsafack⁶, and Jianhua Wu(吴建华)^1,‡

1 Department of Information Engineering, Gongqing College, Nanchang University, Jiujiang 332020, China;
2 School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 511400, China;
3 Department of Electrical Engineering and Industrial Computing, University Institute of Technology, Douala, Cameroon;
4 Department of Computer Engineering, Jamia Millia Islamia, New Delhi 110025, India;
5 Department of Physics, Faculty of Science, University of Yaounde, Cameroon;
6 Electrical Engineering Department and Industrial Computing of ISTAMA, University of Douala, Douala, Cameroon

收稿日期:2023-07-11 修回日期:2023-09-30 接受日期:2023-10-17 出版日期:2024-03-19 发布日期:2024-03-19
通讯作者: Donghua Jiang, Jianhua Wu E-mail:jiangdh8@mail2.sysu.edu.cn;jhwu@ncu.edu.cn

Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

Xinwei Zhou(周新卫)¹, Donghua Jiang(蒋东华)^2,†, Jean De Dieu Nkapkop³, Musheer Ahmad⁴, Jules Tagne Fossi⁵, Nestor Tsafack⁶, and Jianhua Wu(吴建华)^1,‡

1 Department of Information Engineering, Gongqing College, Nanchang University, Jiujiang 332020, China;
2 School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 511400, China;
3 Department of Electrical Engineering and Industrial Computing, University Institute of Technology, Douala, Cameroon;
4 Department of Computer Engineering, Jamia Millia Islamia, New Delhi 110025, India;
5 Department of Physics, Faculty of Science, University of Yaounde, Cameroon;
6 Electrical Engineering Department and Industrial Computing of ISTAMA, University of Douala, Douala, Cameroon

Received:2023-07-11 Revised:2023-09-30 Accepted:2023-10-17 Online:2024-03-19 Published:2024-03-19
Contact: Donghua Jiang, Jianhua Wu E-mail:jiangdh8@mail2.sysu.edu.cn;jhwu@ncu.edu.cn

摘要/Abstract

摘要： Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function, a brand-new tristable locally active memristor model is first proposed in this paper. Here, a novel four-dimensional fractional-order memristive cellular neural network (FO-MCNN) model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its performance. Then, its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation platforms. Subsequently, it is used toward secure communication application scenarios. Taking it as the pseudo-random number generator (PRNG), a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing (ASR-CS) model. Eventually, the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词: cellular neural network, memristor, hardware circuit, compressive sensing, privacy data protection

Abstract: Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function, a brand-new tristable locally active memristor model is first proposed in this paper. Here, a novel four-dimensional fractional-order memristive cellular neural network (FO-MCNN) model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its performance. Then, its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation platforms. Subsequently, it is used toward secure communication application scenarios. Taking it as the pseudo-random number generator (PRNG), a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing (ASR-CS) model. Eventually, the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

Key words: cellular neural network, memristor, hardware circuit, compressive sensing, privacy data protection

中图分类号: (Numerical simulations of chaotic systems)

05.45.Pq

47.20.Ky (Nonlinearity, bifurcation, and symmetry breaking) 87.85.dq (Neural networks) 95.75.Mn (Image processing (including source extraction))

Xinwei Zhou(周新卫), Donghua Jiang(蒋东华), Jean De Dieu Nkapkop, Musheer Ahmad, Jules Tagne Fossi, Nestor Tsafack, and Jianhua Wu(吴建华). Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model[J]. 中国物理B, 2024, 33(4): 40506-040506.

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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