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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 |
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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.
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Received: 11 July 2023
Revised: 30 September 2023
Accepted manuscript online: 17 October 2023
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PACS:
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05.45.Pq
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(Numerical simulations of chaotic systems)
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47.20.Ky
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(Nonlinearity, bifurcation, and symmetry breaking)
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87.85.dq
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(Neural networks)
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95.75.Mn
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(Image processing (including source extraction))
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Corresponding Authors:
Donghua Jiang, Jianhua Wu
E-mail: jiangdh8@mail2.sysu.edu.cn;jhwu@ncu.edu.cn
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Cite this article:
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 2024 Chin. Phys. B 33 040506
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